WO2023157596A1 - Information processing method, information processing device, program, and information processing system - Google Patents

Information processing method, information processing device, program, and information processing system Download PDF

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Publication number
WO2023157596A1
WO2023157596A1 PCT/JP2023/002369 JP2023002369W WO2023157596A1 WO 2023157596 A1 WO2023157596 A1 WO 2023157596A1 JP 2023002369 W JP2023002369 W JP 2023002369W WO 2023157596 A1 WO2023157596 A1 WO 2023157596A1
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information
user
information processing
patients
sensing data
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PCT/JP2023/002369
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French (fr)
Japanese (ja)
Inventor
健治 山根
咲湖 安川
拓 田中
乃愛 金子
律子 金野
能宏 脇田
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ソニーグループ株式会社
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Publication of WO2023157596A1 publication Critical patent/WO2023157596A1/en

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance

Definitions

  • the present disclosure relates to an information processing method, an information processing device, a program, and an information processing system.
  • the information entered by the patient as described above often contains non-objective information, and it is difficult to recommend a suitable medical institution for the patient based on such information.
  • objective information such as test values
  • the disease is a disease other than a disease that can be quantitatively diagnosed based on such information
  • the conventional technology cannot find a suitable medical institution. It can be difficult to find.
  • the present disclosure proposes an information processing method, an information processing device, a program, and an information processing system capable of recommending a suitable medical institution to a patient (user).
  • an information processing device acquires sensing data obtained from a sensor attached to a part of a user's body, and based on the sensing data and treatment performance information of a plurality of patients, extracting the plurality of patients having the sensing data similar to the sensing data of the user from among the plurality of patients;
  • An information processing method includes determining the medical institution to be recommended to the user from among the institutions, and transmitting information on the determined medical institution to an information processing terminal.
  • an acquisition unit that acquires sensing data obtained from a sensor attached to a part of a user's body, the sensing data and treatment performance information of a plurality of patients;
  • the plurality of patients having the sensing data similar to the sensing data of the user is extracted from among the patients, and based on the extracted treatment performance information of the plurality of patients, among the plurality of medical institutions,
  • An information processing apparatus comprising a recommendation unit that determines the medical institution to recommend to the user.
  • the computer has a function of acquiring sensing data obtained from a sensor attached to a part of the user's body, and based on the sensing data and treatment performance information of a plurality of patients, A function of extracting, from among the plurality of patients, the plurality of patients having the sensing data similar to the sensing data of the user;
  • a program is provided for executing a function of determining the medical institution to be recommended to the user from among the institutions and a function of transmitting information on the determined medical institution to an information processing terminal.
  • an information processing system including a sensor attached to a part of a user's body, an information processing device, and an information processing terminal, wherein the information processing device is attached to a part of the user's body. and obtaining the sensing data obtained from the sensors, and selecting the sensing data similar to the sensing data of the user from among the plurality of patients based on the sensing data and treatment performance information of the plurality of patients.
  • a medical institution to be recommended to the user is determined from among a plurality of medical institutions based on the extracted information on the treatment results of the plurality of patients, and the determined medical institution
  • An information processing system is provided for transmitting information to the information processing terminal.
  • FIG. 1 is a system diagram showing a schematic functional configuration of an information processing system 10 according to an embodiment of the present disclosure
  • FIG. 1 is a block diagram showing an example functional configuration of a wearable device 100 according to an embodiment of the present disclosure
  • FIG. 1 is an explanatory diagram showing an example of the appearance of a wearable device 100 according to an embodiment of the present disclosure
  • FIG. 2 is a block diagram showing an example of a functional configuration of a user terminal 200 according to an embodiment of the present disclosure
  • FIG. 3 is a block diagram showing an example of a functional configuration of a server 300 according to an embodiment of the present disclosure
  • FIG. 4 is a sequence diagram of an information processing method according to an embodiment of the present disclosure
  • FIG. 10 is a diagram showing an example of a medical institution information table 362 according to an embodiment of the present disclosure
  • FIG. 1 is a flowchart (Part 1) of an information processing method according to an embodiment of the present disclosure
  • FIG. 4 is an explanatory diagram showing an example of display on the user terminal 200 according to the embodiment of the present disclosure
  • FIG. 2 is a flowchart (part 2) of an information processing method according to an embodiment of the present disclosure
  • FIG. 13 is an explanatory diagram for explaining an information processing method in the importance calculation unit 334 according to the embodiment of the present disclosure
  • FIG. 3 is a flowchart (part 3) of an information processing method according to an embodiment of the present disclosure
  • FIG. 10 is an explanatory diagram (Part 1) for explaining an information processing method in the recommendation degree calculation unit 336 according to the embodiment of the present disclosure
  • FIG. 11 is an explanatory diagram (Part 2) for explaining the information processing method in the recommendation degree calculation unit 336 according to the embodiment of the present disclosure
  • 7 is a flowchart of an information processing method according to Modification 1 of the embodiment of the present disclosure
  • FIG. 13 is an explanatory diagram for explaining an information processing method in the recommendation degree calculation unit 336 according to Modification 1 of the embodiment of the present disclosure
  • FIG. 10 is a flowchart of an information processing method according to Modification 2 of the embodiment of the present disclosure
  • FIG. FIG. 11 is an explanatory diagram for explaining an information processing method in a recommendation degree calculation unit 336 according to Modification 2 of the embodiment of the present disclosure
  • 3 is a block diagram showing an example of hardware configuration
  • Psychiatric disorders are often caused by disorders of brain function, and unlike hypertension, diabetes, etc., it is said to be difficult to detect early by conducting regular and quantitative examinations.
  • it is difficult to establish a consistent treatment protocol for psychiatric disorders because the causes, symptoms, and treatment methods differ for each individual patient. It is not guaranteed that you will receive it. Therefore, it is difficult for patients and their families to find a suitable medical institution for the patient, and in addition, it is difficult for patients with mental illness to make appropriate and calm decisions. Because there are cases, it is still difficult to find a suitable medical institution.
  • the present inventors have developed information capable of recommending a suitable medical institution for treatment of mental illness to a patient using biological information detected by such a sensor.
  • Embodiments of the present disclosure related to processing methods, information processing apparatuses, programs, and information processing systems have been created. The details of such embodiments of the present disclosure will be sequentially described below.
  • a user means a person for whom a medical institution is recommended in the embodiment of the present disclosure.
  • biomarkers mean biometric information that can be routinely detected by noninvasive sensor devices and is considered to be correlated with the user's mental state. Biomarkers are, for example, directly by the sensor device (specifically, the sensing data itself acquired by the sensor device), or indirectly (specifically, the sensing data itself acquired by the sensor device). ).
  • biomarkers are, for example, heart rate, heart rate variability, pulse rate, pulse variability, blood flow, blood oxygen concentration, blood pressure, respiratory volume, respiratory rate, brain waves, perspiration, body temperature, muscle condition, posture , activity level, exercise state, number of steps, distance traveled, sleep time, sleep state (specifically, REM sleep, non-REM sleep, etc.), basal metabolic calorie consumption, calorie consumption due to exercise, biological information such as facial and pupil movements can be
  • interview information means all the information collected from specialists, etc. for diagnosis of patients with mental illness.
  • the interview information includes information such as age, gender, occupation, smoking history, degree of drinking, activity hours, working hours, meal content, family structure, preferences, hobbies, and growth history.
  • the patient's background information on mental status means the subjective evaluation results obtained from specialists for diagnosis of patients with mental illness.
  • the background information is, for example, PHQ-9 (Patient Health Questionnaire-9), GAD-7 (General Anxiety Disorder-7), etc. It means the result of scoring the patient's subjective evaluation of
  • the treatment results mean, for example, information as to whether or not the patient is in remission, treatment period until remission, treatment method, medication information, medical institution in charge, doctor in charge, etc. and
  • FIG. 1 is a system diagram showing a schematic functional configuration of an information processing system 10 according to an embodiment of the present disclosure.
  • an information processing system 10 includes a user terminal (information processing terminal) 200 communicably connected to a wearable device 100, a server (information processing device) 300, and a medical institution terminal. 400 , which are communicably connected to each other via a network 500 .
  • the user terminal 200, the server 300, and the medical institution terminal 400 are connected to the network 500 via a base station (for example, a mobile phone base station, a wireless LAN (Local Area network) access point, etc.) (not shown).
  • a base station for example, a mobile phone base station, a wireless LAN (Local Area network) access point, etc.
  • the communication method used in the network 500 can be any method regardless of whether it is wired or wireless (for example, WiFi (registered trademark), Bluetooth (registered trademark), etc.), but stable operation is maintained. It is desirable to use a communication method that can Below, an outline of each device included in the information processing system 10 according to the present embodiment will be described.
  • Wearable device 100 can be a device that can be worn on a user's body part (face, earlobe, neck, arm, wrist, ankle, etc.). More specifically, the wearable device 100 is a head mounted display (HMD) type, eyeglass type, ear device type, anklet type, bracelet (wristband) type, collar type, eyewear type, pad type, batch type, clothes It can be a wearable device of various types such as a type, a hat type, and a mask type.
  • HMD head mounted display
  • eyeglass type eyeglass type
  • ear device type anklet type
  • bracelet bracelet
  • collar type eyewear type
  • pad type a wearable device of various types
  • clothes can be a wearable device of various types such as a type, a hat type, and a mask type.
  • the wearable device 100 has a sensor section 140 (see FIG. 2), which is a non-invasive sensor device capable of acquiring the user's biomarkers.
  • the sensor unit 140 includes, for example, a blood flow sensor that detects the user's pulse, heart rate, blood flow, intermittent oxygen, etc., an ECG (Electrocardiogram) sensor that detects the user's electrocardiogram, a blood pressure sensor that detects the user's blood pressure, a user A perspiration sensor that detects the perspiration of the user, an electroencephalogram sensor that detects the user's electroencephalogram (and can indirectly detect the user's state of sleep, relaxation, etc.
  • ECG Electrocardiogram
  • a body temperature sensor that detects the user's body temperature
  • a user's A myoelectric potential sensor that detects the tension state of muscles
  • a respiration sensor that detects the user's respiration rate and respiration volume, and the like may be provided.
  • the sensor unit 140 may also include a motion sensor for detecting the user's posture and movement, that is, the state of exercise and the state of activity.
  • the motion sensor for example, acquires sensing data indicating changes in acceleration that occur with the motion of the user, and performs analysis processing as necessary to determine the user's exercise state, posture, number of steps, distance traveled, and amount of activity. , sleep state, sleep time, energy consumption, basal metabolic energy consumption, and the like can be detected.
  • the sensor unit 140 includes an imaging device (not shown) that captures the user's expression, face, line of sight, and pupil movement, and a microphone that acquires the user's voice (hereinafter referred to as a microphone). (illustration omitted) etc. may be included.
  • the wearable device 100 is assumed to be, for example, a bracelet (wristband) type wearable device. Details of the wearable device 100 will be described later.
  • the user terminal 200 is a terminal that the user uses on a daily basis, and is capable of transmitting biomarkers from the wearable device 100 to the server 300 described below and receiving information from the server 300 .
  • the user terminal 200 can be a device such as a tablet, a smart phone, a mobile phone, a laptop PC (Personal Computer), a desktop PC, or a Head Mounted Display (HMD).
  • the user terminal 200 shall be a smart phone. Details of the user terminal 200 will be described later.
  • the server 300 Based on the biomarkers obtained from the wearable device 100 via the user terminal 200 and the information input by the user using the user terminal 200, the server 300 determines and determines the medical institution or the like to be recommended to the user. Information can be provided to the user.
  • the server 300 is configured by, for example, a computer. A detailed configuration of the server 300 will be described later.
  • the medical institution terminal 400 is a terminal used by medical personnel such as doctors at the medical institution, and can transmit information to the server 300 via the network 500 .
  • the medical institution terminal 400 can be a device such as a tablet, smart phone, mobile phone, laptop PC, or desktop PC. In the following description, medical institution terminal 400 is assumed to be a desktop PC. Details of the medical institution terminal 400 will be described later.
  • FIG. 1 shows the information processing system 10 according to the present embodiment as including a pair of wearable device 100 and user terminal 200
  • the present embodiment is not limited to this. do not have.
  • the information processing system 10 according to the present embodiment may include one or more pairs of multiple wearable devices 100 and one or multiple user terminals 200 .
  • the information processing system 10 may include a plurality of medical institution terminals 400 .
  • the information processing system 10 according to the present embodiment includes other communication devices such as relay devices for transmitting and receiving information between the plurality of user terminals 200 and the plurality of medical institution terminals 400 and the server 300, for example. etc. may be included.
  • FIG. 2 is a block diagram showing an example of the functional configuration of the wearable device 100 according to this embodiment.
  • the wearable device 100 mainly has an input unit 110, an output unit 120, a control unit 130, a sensor unit 140, a communication unit 150, and a storage unit 160, as shown in FIG. Details of each functional unit of the wearable device 100 will be described below.
  • the input unit 110 receives input of data and commands from the user to the wearable device 100 . More specifically, the input unit 110 is implemented by a touch panel, buttons, a microphone, and the like.
  • the output unit 120 is a device for presenting information to the user, and for example, outputs various information to the user using images, sounds, lights, vibrations, or the like. More specifically, the output unit 120 can display information provided from the server 300, which will be described later, on the screen.
  • the output unit 120 is implemented by a display, a speaker, earphones, a light-emitting element (for example, a Light Emitting Diode (LED)), a vibration module, and the like. Note that part of the functions of the output unit 120 may be provided by the user terminal 200 .
  • control unit 130 The control unit 130 is provided in the wearable device 100, controls each functional unit of the wearable device 100, acquires sensing data (biomarkers) output from the sensor unit 140 described later, and analyzes the sensing data. can be processed.
  • the control unit 130 is realized by hardware such as a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), and the like. Note that part of the functions of the control unit 130 may be provided by the user terminal 200, which will be described later.
  • the sensor unit 140 is provided in the wearable device 100 attached to the user's body, and has various sensors that detect the user's biological information.
  • the sensor unit 140 is, for example, a PPG sensor (pulse sensor) (not shown) that detects the pulse or heartbeat of the user and acquires time-series data of the heartbeat or pulse, or a sensor that detects the movement of the user. It has a motion sensor (not shown) and the like.
  • the PPG sensor is a biosensor worn on a part of the body such as the user's skin (for example, both arms, wrists, ankles, etc.) in order to detect the user's pulse wave signal.
  • the pulse wave signal refers to the contraction of the heart muscle at a constant rhythm (pulsation, the number of heart beats per unit time is called the heart rate), and blood is sent throughout the body through the arteries.
  • a change in pressure occurs on the inner wall of the artery, and it is a waveform due to the pulsation of the artery that appears on the body surface.
  • the PPG sensor In order to acquire a pulse wave signal, the PPG sensor irradiates light on the blood vessels in the measurement site of the user, such as a hand, arm, leg, etc. Detect scattered light. Since the irradiated light is absorbed by red blood cells in the blood vessel, the amount of light absorption is proportional to the amount of blood flowing through the blood vessel in the measurement site. Therefore, the PPG sensor can detect changes in the amount of flowing blood by detecting the intensity of the scattered light. Furthermore, a pulsation waveform, that is, a pulse wave signal can be detected from this change in blood flow. Such a method is called PhotoPlethysmoGraphy (PPG) method.
  • PPG PhotoPlethysmoGraphy
  • the PPG sensor incorporates a small laser or LED (Light Emitting Diode) (not shown) that can irradiate coherent light, and irradiates light with a predetermined wavelength such as around 850 nm, for example. .
  • the wavelength of the light emitted by the PPG can be selected as appropriate.
  • the PPG sensor incorporates, for example, a photodiode (Photo Detector: PD), and acquires a pulse wave signal by converting the intensity of the detected light into an electrical signal.
  • the PPG sensor may incorporate a CCD (Charge Coupled Devices) type sensor, a CMOS (Complementary Metal Oxide Semiconductor) type sensor, or the like instead of the PD.
  • the PPG sensor may also include optical mechanisms such as lenses and filters to detect light from the user's measurement site.
  • the PPG sensor can detect the pulse wave signal as time-series data having multiple peaks.
  • a peak interval between a plurality of peaks appearing in a pulse wave signal is called a pulse rate interval (PPI).
  • PPI pulse rate interval
  • the PPI value can be obtained by processing the pulse wave signal detected by the PPG sensor. It is known that each PPI value fluctuates over time, but is approximately normally distributed while the user's state is maintained constant. Therefore, by statistically processing the PPI value data group (for example, calculating the standard deviation of the PPI value), various HRV (Heart Rate Variability) indicators of the user's mental state (for example, Stre's state) An index can be calculated. Therefore, time-series data of heartbeat or pulse can serve as an indicator of the user's mental state.
  • HRV Heart Rate Variability
  • the present embodiment is not limited to obtaining pulse wave signals using the PPG method described above, and pulse wave signals may be obtained by other methods.
  • the sensor unit 140 may detect pulse waves using a laser Doppler blood flow measurement method.
  • the laser Doppler blood flow measurement method is a method of measuring blood flow using the following phenomena. Specifically, when the user's measurement site is irradiated with laser light, scattering substances (mainly red blood cells) present in the user's blood vessels move, causing scattered light with a Doppler shift. . Then, the scattered light accompanied by the Doppler shift interferes with the scattered light from non-moving tissue present in the measurement site of the user, and a beat-like intensity change is observed. Therefore, the laser Doppler blood flow measurement method can measure blood flow by analyzing the intensity and frequency of the beat signal.
  • an ECG sensor that detects the electrocardiogram of the target user via electrodes (not shown) attached to the user's body may be provided.
  • the RR interval which is the interval between heart beats
  • the HRV index which is an index indicating the mental state of the user
  • the sensor unit 140 may be provided with a perspiration sensor (not shown).
  • a perspiration sensor (not shown).
  • thermal perspiration is sweating that is done to regulate body temperature.
  • mental sweating is sweating caused by human emotions such as tension and emotions. It is sweating that occurs instantaneously and in a small amount on the palms and soles of the feet at room temperature compared to thermal sweating.
  • mental sweating is sweating caused by tension when giving a presentation.
  • Such mental sweating is known to occur frequently when the sympathetic nervous system is dominant, and is generally considered to be an indicator of mental state.
  • the perspiration sensor is attached to the user's skin and detects the voltage or resistance between two points on the skin that changes due to perspiration.
  • information such as the amount of perspiration, the frequency of perspiration, and changes in the amount of perspiration is obtained based on the sensing data detected by the perspiration sensor, thereby obtaining one index indicating the mental state of the user. be able to.
  • the sensor unit 140 may include other various biosensors (not shown).
  • the various biosensors may include one or more sensors attached directly or indirectly to a part of the user's body and measuring the target user's electroencephalogram, respiration, myoelectric potential, skin temperature, etc. can.
  • sensing data obtained by an electroencephalogram sensor unit (not shown) that measures the electroencephalogram of the user is analyzed to determine the type of electroencephalogram (e.g., alpha waves, beta waves, etc.), it is possible to obtain an index indicating the mental state of the user (for example, the degree of relaxation of the user, etc.).
  • the sensor unit 140 may include an imaging device (not shown) that captures the user's facial expression, as described above.
  • the imaging device detects, for example, the user's eye movement, pupil diameter, gaze time, and the like. It is said that the muscle that controls the human pupil diameter is influenced by the sympathetic/parasympathetic nerves. Therefore, in this embodiment, by detecting the user's pupil diameter with the imaging device, it is possible to obtain one index indicating the user's mental state, such as the user's sympathetic/parasympathetic state. .
  • the sensor unit 140 may also include a motion sensor for detecting the user's posture and movement, that is, the motion state.
  • the motion sensor detects the motion state of the user by acquiring sensing data indicating changes in acceleration that occur with the motion of the user.
  • the user's exercise state acquired by the motion sensor can be used when estimating the user's emotion.
  • the posture affects the depth of breathing and the like, and furthermore, the depth of breathing is said to be highly related to the state of tension (degree of tension) of a person. Therefore, in the present embodiment, it is possible to detect the user's posture and obtain one index indicating the user's mental state from the detected posture.
  • the motion sensor includes an acceleration sensor, a gyro sensor, a geomagnetic sensor, etc. (not shown).
  • the motion sensor may be an imaging device (not shown) that captures an image of the user.
  • the motion sensor may include an infrared sensor, an ultrasonic sensor, or the like (not shown) capable of detecting user motion. Note that such an imaging device, an infrared sensor, and the like may be installed around the user as separate devices from the wearable device 100 .
  • the sensor unit 140 may include a positioning sensor (not shown) together with the motion sensor.
  • the positioning sensor is a sensor that detects the position of the user wearing the wearable device 100, and specifically can be a GNSS (Global Navigation Satellite System) receiver or the like.
  • the positioning sensor can generate sensing data indicating the latitude and longitude of the user's current location based on signals from GNSS satellites.
  • RFID Radio Frequency Identification
  • Wi-Fi since it is possible to detect the relative positional relationship of the user from the information of the wireless base station, such It is also possible to use a communication device as the positioning sensor.
  • the sensor unit 140 may include a microphone (not shown) that detects the user's uttered voice.
  • a microphone not shown
  • the result obtained by extracting a specific voice (for example, a specific phrase uttered by the user) from the sound detected by the microphone is used as one index indicating the mental state of the user. can be obtained as
  • the sensor unit 140 can include various sensors. Furthermore, the sensor unit 140 may incorporate a clock mechanism (not shown) that grasps the correct time, and associate the time when the sensing data (biomarker) is acquired with the acquired sensing data. Further, as described above, the various sensors may not be provided in the sensor unit 140 of the wearable device 100. For example, they may be provided separately from the wearable device 100. may be provided in a device or the like used by
  • the communication unit 150 is provided within the wearable device 100 and can transmit and receive information to and from an external device such as the user terminal 200 .
  • the communication unit 150 can be said to be a communication interface having a function of transmitting and receiving data.
  • the communication unit 150 is implemented by communication devices such as a communication antenna, a transmission/reception circuit, and a port.
  • the storage unit 160 is provided in the wearable device 100 and stores programs, information, etc. for the above-described control unit 130 to execute various processes, and information obtained by the processes.
  • the storage unit 160 is realized by, for example, a nonvolatile memory such as a flash memory.
  • FIG. 3 shows an example of the appearance of the wearable device 100 according to this embodiment.
  • the wearable device 100 is a bracelet-type wearable device worn on the user's wrist.
  • the wearable device 100 has a belt-shaped band portion 170 and a control unit 180 .
  • the band part 170 is worn around the user's wrist, it is made of a soft material such as silicone gel so as to have a ring-like shape that matches the shape of the wrist.
  • the control unit 180 is a portion where the above-described control section 130, sensor section 140, and the like are provided.
  • the sensor unit 140 is provided at a position such that when the wearable device 100 is worn on a part of the user's body, it contacts or faces the user's body.
  • the wearable device 100 according to the present embodiment is not limited to the configuration example shown in FIG. 2 or the appearance shown in FIG. .
  • FIG. 4 is a block diagram showing an example of the functional configuration of the user terminal 200 according to this embodiment.
  • the user terminal 200 according to this embodiment mainly includes an input unit 210, an output unit 220, a control unit 230, a communication unit 250, and a storage unit 260, as shown in FIG.
  • Each functional unit of the user terminal 200 will be described below.
  • the input unit 210 can accept input of data and commands to the user terminal 200 . More specifically, the input unit 210 is implemented by a touch panel, keyboard, microphone, or the like.
  • the output unit 220 is composed of, for example, a display, a speaker, a lamp, a video output terminal, an audio output terminal, etc., and can output various information to the user by means of images, flashes, sounds, and the like.
  • control unit 230 The control unit 230 is provided in the user terminal 200 and can control each functional unit of the user terminal 200 and acquire biomarkers (sensing data) from the wearable device 100 .
  • the control unit 230 is realized by, for example, a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), and the like. Also, the control unit 230 may perform analysis processing on sensing data from the wearable device 100 .
  • the communication unit 250 can transmit and receive information to and from external devices such as the wearable device 100 and the server 300 .
  • the communication unit 250 can be said to be a communication interface having a function of transmitting and receiving data.
  • the communication unit 250 is implemented by communication devices such as a communication antenna, a transmission/reception circuit, and a port.
  • the storage unit 260 can store programs, information, etc. for the above-described control unit 230 to execute various processes, and information obtained by the processes.
  • the storage unit 260 is realized by, for example, a magnetic recording medium such as a hard disk (HD), a nonvolatile memory such as a flash memory, or the like.
  • the user terminal 200 according to the present embodiment is not limited to the configuration example shown in FIG. 4, and may further include other functional units, for example.
  • FIG. 5 is a block diagram showing an example of the functional configuration of the server 300 according to this embodiment.
  • the server 300 mainly includes an input unit 310, an output unit 320, a processing unit 330, a communication unit 350, and a storage unit 360, as shown in FIG.
  • Each functional unit of the server 300 will be described below.
  • the input unit 310 can accept input of data and commands to the server 300 . More specifically, the input unit 310 is implemented by a touch panel, keyboard, or the like.
  • the output unit 320 is configured by, for example, a display or the like, and can output various information in the form of images or the like.
  • the processing unit 330 can control each functional unit of the server 300 .
  • the processing unit 330 is realized by hardware such as CPU, ROM, and RAM, for example.
  • the processing unit 330 can determine a medical institution to recommend to the user based on the biomarkers and the like from the wearable device 100 .
  • the processing unit 330 can also extract items with high importance.
  • the processing unit 330 functions as an acquisition unit 332, an importance calculation unit 334, a recommendation calculation unit (recommendation unit) 336, and an output control unit 338 in order to realize these functions described above.
  • the processing unit 330 may also perform analysis processing on sensing data from the wearable device 100 . Details of these functions of the processing unit 330 according to the present embodiment will be described below.
  • the acquisition unit 332 acquires biomarkers transmitted from the wearable device 100 via the user terminal 200, interview information, background information (scores), and the like from the user terminal 200.
  • the acquiring unit 332 acquires information (biomarkers, interview information, background information, treatment results, etc.) of a plurality of patients from each medical institution terminal 400 .
  • the acquisition unit 332 can output the acquired information to the importance calculation unit 334 and the recommendation calculation unit 336, which will be described later.
  • the importance calculation unit 334 uses a technique such as multivariate analysis for a plurality of patient information (biomarkers, medical interview information, background information, treatment results, etc.) obtained from a plurality of medical institutions, for each information , the degree of importance, which is an index indicating the degree of high correlation (relevance) to remission (more specifically, whether or not remission has occurred) can be calculated. Furthermore, the importance calculation unit 334 can search for information items (types) highly correlated with remission based on the calculated importance.
  • the importance calculation unit 334 outputs information such as the importance of each information (item) and the ranking of the information (items) arranged according to the importance to the output control unit 338 and the storage unit 360, which will be described later. can be done.
  • the information output by the importance calculation unit 334 can be used when requesting the user to input information, or used when determining the number of patients to be extracted by the recommendation calculation unit 336, which will be described later. can. Details of the operation of the importance calculation unit 334 will be described later.
  • the recommendation degree calculation unit 336 analyzes biomarkers (sensing data) transmitted from the wearable device 100 via the user terminal 200, treatment results obtained from a plurality of medical institutions, etc., and provides the user with treatment for mental illness. can decide which medical institution to recommend for Then, the recommendation degree calculation unit 336 can output information on the determined medical institution to the output control unit 338 described later. For example, the recommendation degree calculation unit 336 selects a user's A degree of similarity indicating the degree of similarity to the biomarker is calculated, and a predetermined number of patients are extracted in descending order of the calculated degree of similarity, thereby extracting patients similar to the user.
  • the recommendation degree calculation unit 336 calculates the recommendation degree of each medical institution based on the treatment results of a plurality of extracted patients (for example, consultation rate, remission rate, etc.), and sends the highly recommended medical institution to the user. Decide as a recommended medical institution. Details of the operation of the recommendation degree calculation unit 336 will be described later.
  • Output control unit 338 controls the communication unit 350, which will be described later, to transmit the information output by the importance calculation unit 334 and the recommendation calculation unit 336, information selected based on the information, and the like to the user terminal 200. can do.
  • the communication unit 350 can transmit and receive information to and from external devices such as the user terminal 200 and the medical institution terminal 400 .
  • the communication unit 350 can be said to be a communication interface having a function of transmitting and receiving data, and is specifically realized by a communication device such as a communication antenna, a transmission/reception circuit, and a port.
  • the storage unit 360 can store programs, information, and the like for the processing unit 330 described above to execute various types of processing, and information obtained by the processing. Specifically, as shown in FIG. 5, the storage unit 360 stores the treatment results obtained from each medical institution (for example, information on whether the patient is in remission, treatment period until remission, treatment method, medication information, A medical institution information table 362 can be stored that stores information such as doctors in charge. The storage unit 360 can also store an importance ranking table 364 that stores the ranking of information arranged according to the importance calculated by the recommendation calculation unit 336 described above. Furthermore, the storage unit 360 can store a user information table 366 that stores various types of user information (eg, biomarkers, interview information, background information) obtained from the user terminal 200 .
  • user information table 366 that stores various types of user information (eg, biomarkers, interview information, background information) obtained from the user terminal 200 .
  • the storage unit 360 is implemented by, for example, a magnetic recording medium such as a hard disk, or a non-volatile memory such as a flash memory.
  • the above information includes information related to the privacy of the user and the patient, in the present embodiment, the above information is temporarily stored in the storage unit 360 only when processing is performed by the processing unit 330, It is preferable to erase immediately after the processing is finished.
  • information processing for example, using identification information consisting of a simple character string instead of naming a patient is performed so that individual patients cannot be identified.
  • the server 300 is not limited to the configuration example shown in FIG. 5, and may further include other functional units, for example. Furthermore, the server 300 may be composed of a plurality of information processing devices communicably connected to each other via the network 500 . At least part of the functions of the server 300 may be executed by the user terminal 200 described above, or the server 300 may be configured as a device integrated with the user terminal 200 or the medical institution terminal 400. .
  • FIG. 6 is a sequence diagram of the information processing method according to this embodiment
  • FIG. 7 is a diagram showing an example of the medical institution information table 362 according to this embodiment.
  • 8, 10 and 12 are flowcharts of the information processing method according to this embodiment
  • FIG. 9 is an explanatory diagram showing an example of display on the user terminal 200 according to this embodiment.
  • FIG. 11 is an explanatory diagram for explaining the information processing method in the importance calculation unit 334 according to this embodiment
  • FIGS. It is an explanatory view for explaining an information processing method.
  • the information processing method according to this embodiment includes a plurality of steps from step S100 to step S1100. Details of each step included in the information processing method according to the present embodiment will be described below.
  • the user terminal 200 acquires user information including background information (score) about the user, interview information, biomarker data, etc., and transmits the acquired information to the server 300 (step S100). Details of step S100 will be described later.
  • the server 300 receives the user information from the user terminal 200 (step S200).
  • the medical institution terminal 400 receives patient treatment results (for example, information on whether or not the patient is in remission, treatment period until remission, treatment method, medication information, information of the doctor in charge, etc.), biomarkers at the start of the medical examination, background information (score), interview information, etc. are acquired (step S300).
  • patient treatment results for example, information on whether or not the patient is in remission, treatment period until remission, treatment method, medication information, information of the doctor in charge, etc.
  • biomarkers at the start of the medical examination for example, background information (score), interview information, etc.
  • background information background information
  • medical interview information for example, information on whether the patient is in remission, treatment period until remission, treatment method, medication information, doctor in charge, etc. information
  • the treatment results may also include patient subjective information, such as the patient's degree of satisfaction with treatment and the degree of recovery after treatment.
  • patient's subjective information can be collected by a medical institution, an operating agency of the information processing system according to the present embodiment, or the like, through a regular or non-periodic questionnaire to the patient.
  • the biomarkers stored in the medical institution information table 362 are not limited to two types of biomarkers as shown in FIG. good.
  • the background information (score) and medical inquiry information stored in the medical institution information table 362 are limited to one type of background information (score) and medical inquiry information as shown in FIG. It may be multiple types of background information (scores) and interview information.
  • the timing of the processing in step S300 is not particularly limited, and may be executed each time the treatment information is updated at the medical institution, or may be executed periodically at predetermined intervals. .
  • the server 300 acquires the medical institution information table 362 described above from the medical institution terminal 400 (step S400). As described above, the above-described information includes a lot of information related to patient privacy. It is preferable to acquire the copy data of the information table 362 and delete the copy data after the processing is completed.
  • the server 300 may directly acquire the medical institution information table 362 or various information contained therein without going through the medical institution terminal 400 (more specifically, the server 300 may information is entered directly).
  • the server 300 uses techniques such as multivariate analysis on the treatment information obtained from a plurality of medical institutions in step S400 described above to determine remission (specifically, remission or not) for each type (item) of information. or not) is calculated (step S500). Furthermore, the server 300 generates an importance ranking table 364 that ranks information (items) arranged according to importance.
  • the timing of the processing in step S500 is not particularly limited, but may be performed each time treatment information is updated at a medical institution, or may be performed periodically at predetermined intervals. good too. Details of step S500 will be described later.
  • Server 300 receives the types (items) of information included in the top B% of importance ranking table 364 generated in step S500 described above, that is, information with high importance, from user terminal 200 in step S200 described above. It is determined whether it is included in the information (whether it is available) (step S600). If server 300 determines that it is available (step S600: Yes), the process proceeds to step S1000. On the other hand, when the server 300 determines that it is not available (step S600: No), it proceeds to the process of step S600.
  • the server 300 transmits to the user terminal 200 an instruction requesting the input of information (items) of high importance (step S700). Specifically, the server 300 requests information that has not been received from the user terminal 200 in step S200 among the information included in the top B% of the importance ranking table 364 . In this embodiment, by making such a request, the server 300 can acquire information with a high degree of importance, and therefore can recommend a more suitable medical institution to the user based on the acquired information. . In the present embodiment, it is assumed that the operator can appropriately set and change the upper range of information in the importance ranking table 364 in which information with a high degree of importance is to be placed.
  • the user terminal 200 receives the request from the server 300 (step S800). Further, based on the request, the user terminal 200 prompts the user to input the relevant information by using highlighted display, output of an alarm sound, or the like. Then, the user terminal 200 acquires the additional information input by the user and transmits it to the server 300 (step S900).
  • the server 300 determines a medical institution to recommend to the user based on various information acquired from the user terminal 200 in the steps executed so far, and transmits the determined information to the user terminal 200 (step S1000). Details of step S1000 will be described later. Then, the user terminal 200 displays the information (recommendation information) of the medical institution recommended to the user, received from the server 300, to the user (step S1100).
  • the server 300 sends the name of the actually received medical institution to the user via the user terminal 200 after a predetermined period of time has passed from step S1100, A questionnaire may be conducted asking about the degree of satisfaction with the treatment, the degree of recovery after the treatment is completed, and the like. At this time, the information obtained is accumulated in the server 300 as treatment performance information.
  • the information processing method (in detail, step S100 in FIG. 6) performed by the user terminal 200 according to the present embodiment will be described.
  • the information processing method according to this embodiment includes a plurality of steps from step S101 to step S104. Details of each step included in the information processing method according to the present embodiment will be described below.
  • the user terminal 200 presents the contents of the inquiry and prompts the user to input (step S101). Further, the user terminal 200 presents a question about the patient's background information regarding the mental state as shown in FIG. 9 and prompts the user to input, as in step S101 described above (step S102). For example, the user can answer the question 600 by pressing a button 602 displayed simultaneously with the question 600 .
  • the user terminal 200 uploads biomarker data from the wearable device 100 (step S103). Then, the user terminal 200 transmits the information acquired in steps S101 to S103 described above to the server 300 (step S104).
  • the information processing method (Information processing method performed by the importance calculation unit 334 of the server 300 according to the present embodiment) Next, the information processing method (in detail, step S500 in FIG. 6) performed by the importance calculation unit 334 according to the present embodiment will be described with reference to FIGS. 10 and 11.
  • FIG. 10 the information processing method according to this embodiment includes a plurality of steps from step S501 to step S504. Details of each step included in the information processing method according to the present embodiment will be described below.
  • the server 300 can more reliably acquire information that is strongly correlated (highly important) with remission from the user, and thus can recommend a more suitable medical institution to the user. can.
  • the server 300 collects treatment information of all medical institutions (patient treatment results (for example, information on whether or not the patient is in remission, treatment period until remission, treatment method, medication information, information on the doctor in charge, etc.) , biomarkers, background information (score), and interview information at the start of medical examination) are acquired (step S501).
  • patient treatment results for example, information on whether or not the patient is in remission, treatment period until remission, treatment method, medication information, information on the doctor in charge, etc.
  • biomarkers for example, information on whether or not the patient is in remission, treatment period until remission, treatment method, medication information, information on the doctor in charge, etc.
  • biomarkers background information
  • background information interview information at the start of medical examination
  • the server 300 sets, as an inference result, information on whether or not the patient's mental illness is in remission, among the treatment information acquired in step S501 (step S502).
  • the server 300 uses the information on each item other than the information on whether or not the patient is in remission as a variable, and uses a machine learning technique to calculate the importance of each item (step S503).
  • a machine learning technique For example, methods such as random forest, LightGBM, and the like can be used as methods for calculating the degree of importance.
  • the present embodiment is not limited to a random forest or the like, and is not particularly limited as long as it is a method that can perform supervised learning and calculate the importance of variables.
  • the importance calculation unit 334 of the server 300 is assumed to be a supervised learning device such as support vector regression or deep neural network. Then, for example, as shown in FIG. 11, an information group consisting of various biomarkers 1-a, 2-a, scores (background information) 1-a, 2-a, etc. of the patient a is stored in the importance calculation unit 334. , and information on whether or not patient a is in remission (remission a). Furthermore, in the same way, input information groups of multiple patients (biomarkers 1-n, 2-n, scores 1-n, 2-n, etc.) and information on whether each patient is in remission (remission n) . Then, the importance calculation unit 334 performs machine learning on the relationship between each piece of information in the information group and remission information according to a predetermined rule, and calculates the degree of correlation with remission, that is, the importance.
  • a supervised learning device such as support vector regression or deep neural network.
  • the server 300 Based on the importance calculated in step S503, the server 300 generates an importance ranking table 364 in which each piece of information (item) is arranged according to its importance (step S504).
  • FIG. 12 the information processing method according to this embodiment includes a plurality of steps from step S901 to step S906. Details of each step included in the information processing method according to the present embodiment will be described below.
  • the server 300 determines the target medical institution in advance. For example, it may be all medical institutions, it may be limited to medical institutions located within a range that the user can visit, or conditions specified in advance by the user (for example, size, number of affiliated doctors, The target medical institutions may be limited according to the number of affiliated medical professionals, facilities, etc.).
  • the server 300 selects one medical institution from the target medical institutions (step S901).
  • the server 300 calculates the degree of similarity with the user regarding the interview information or background information for the patients in the data table of the target medical institution, and patients are extracted and their remission rate is calculated (step S902). Specifically, the server 300 uses patient interview information included in the treatment information of the medical institution selected in step S901 described above, for example, to generate a similarity index indicating how similar each patient is to the user's interview information. Calculate degrees. Furthermore, the server 300 selects M patients in descending order of similarity, and calculates the remission rate of the selected patients.
  • M persons can be arbitrarily set, for example, may be determined based on the degree of importance calculated by the importance degree calculation unit 334, or may be set in advance by the user.
  • similarity can be calculated using, for example, a difference in Euclidean distance, a cosine similarity, or the like, but is not particularly limited. For example, as shown in FIG. 13, assuming that M is 100, the remission rate is 10% if 10 of them are in remission, as shown in FIG.
  • step S902 may be performed for both the medical interview information and the background information, or the process of step S902 may be omitted.
  • the server 300 calculates the similarity with the user in terms of biomarkers for patients in the data table of the target medical institution, extracts L patients with high similarity, and calculates the remission rate. Calculate (step S903). Specifically, the server 300 uses the patient's biomarkers included in the treatment data of the medical institution selected in step S901 described above to calculate the degree of similarity that indicates how similar each patient is to the user's biomarkers. calculate. Furthermore, the server 300 selects L patients in descending order of similarity and calculates the remission rate of the selected patients.
  • the L persons can be arbitrarily set, and for example, may be determined based on the importance calculated by the importance calculation unit 334, or may be set in advance by the user.
  • the similarity can be calculated using, for example, a difference in Eugrid distance, a cosine similarity, or the like, but is not particularly limited. For example, as shown in FIG. 13, assuming that L is 100 and 90 of them are in remission, the remission rate is 90%.
  • the server 300 calculates the recommendation level of the medical institution selected in step S901 (step S904). For example, as shown in FIG. 13, when the degree of remission of three items (interview information, background information, and biomarker) is calculated, the average value thereof may be used as the degree of recommendation. Specifically, in the medical institution A shown in FIG. 13, the average value is 40%, so the recommendation level is set to 40. Note that in the present embodiment, the calculation of the recommendation level is not limited to the average value, and the average value may be calculated after weighting each item.
  • the server 300 determines whether the degrees of recommendation for all target medical institutions have been calculated (step S905). If the server 300 determines that the recommendation levels of all target medical institutions have been calculated (step S905: Yes), the process proceeds to step S906. On the other hand, when the server 300 determines that the recommendation levels of all target medical institutions have not been calculated (step S905: No), the process returns to step S901. In this embodiment, the recommendation degrees of all target medical institutions are calculated in this way.
  • the server 300 recommends medical institutions with a high degree of recommendation to the user (step S906). For example, in the example shown in FIG. 14, the server 300 recommends the medical institution B, which has the highest recommendation degree of 70, because the medical institution B has the highest recommendation degree.
  • biomarkers that can be easily and routinely detected, it is possible to recommend a medical institution suitable for treatment of mental illness to the user. can.
  • step S906 if a plurality of medical institutions have the same recommendation level, a plurality of medical institutions may be recommended, or the number of patients to be extracted (M person, L person, etc.) may be reset to a different value, and then the above-described information processing method may be performed again.
  • the information processing method performed by the recommendation degree calculation unit 336 is based on the similarity between all patients and users at all medical institutions, instead of calculating the remission rate for each medical institution.
  • a medical institution that has a track record of treating patients similar to the user may be recommended to the user. Therefore, the details of the modified example 1 of the embodiment of the present disclosure will be described below with reference to FIGS. 15 and 16.
  • FIG. FIG. 15 is a flowchart of the information processing method according to Modification 1 of the present embodiment
  • FIG. 16 is an explanation for explaining the information processing method in the recommendation level calculation unit 336 according to Modification 1 of the present embodiment. It is a diagram.
  • the information processing method according to Modification 1 includes a plurality of steps from step S911 to step S915. Details of each step included in the information processing method according to Modification 1 will be described below.
  • the server 300 acquires treatment information from all medical institutions, and based on the user's interview information, calculates the degree of similarity with the user regarding the interview information for patients in the data tables of all medical institutions. Further, the server 300 extracts M patients with a high degree of similarity, and calculates the consultation rate of the extracted patients for each medical institution (step S911).
  • the server 300 acquires treatment information of all medical institutions, and based on the user's background information, calculates the degree of similarity with the user in terms of background information for patients in the data tables of all medical institutions. Furthermore, the server 300 extracts L patients with a high degree of similarity, and calculates the consultation rate of the extracted patients for each medical institution (step S912).
  • the server 300 acquires the treatment information of all medical institutions, and based on the user's biomarkers, calculates the degree of similarity with the user in terms of biomarkers for patients in the data tables of all medical institutions. Furthermore, the server 300 extracts A patients with a high degree of similarity, and calculates the consultation rate of the extracted patients for each medical institution (step S913).
  • the server 300 uses the consultation rates calculated in steps S911 to S913 described above to extract the medical institution with the highest consultation rate for each interview information, background information, and biomarker (step S914).
  • the server 300 recommends to the user the medical institution with the highest consultation rate among the medical institutions extracted in step S914 described above (step S915).
  • the consultation rate of medical institution B which has the highest consultation rate in the medical interview information items
  • the consultation rate of medical institution A which has the highest consultation rate in the biomarker items
  • the medical institution to be recommended may be determined according to a preset priority item. You may make it
  • the server 300 is preset to give priority to medical interview information, so it compares consultation rates in the medical interview information and selects medical institution B with the highest consultation rate (recommendation level). do.
  • FIG. 17 is a flowchart of the information processing method according to Modification 2 of the present embodiment
  • FIG. 18 is an explanation for explaining the information processing method in the recommendation degree calculation unit 336 according to Modification 2 of the present embodiment. It is a diagram.
  • the information processing method according to Modification 2 includes a plurality of steps from step S921 to step S925. Details of each step included in the information processing method according to Modification 2 will be described below.
  • the server 300 acquires treatment information from all medical institutions, and based on the user's interview information, calculates the degree of similarity with the user regarding the interview information for patients in the data tables of all medical institutions. Furthermore, the server 300 extracts M patients with a high degree of similarity, and calculates the remission rate of the extracted patients for each medical institution (step S921).
  • the server 300 acquires treatment information of all medical institutions, and based on the user's background information, calculates the degree of similarity with the user in terms of background information for patients in the data tables of all medical institutions. Furthermore, the server 300 extracts L patients with a high degree of similarity, and calculates the remission rate of the extracted patients for each medical institution (step S922).
  • the server 300 acquires the treatment information of all medical institutions, and based on the user's biomarkers, calculates the degree of similarity with the user in terms of biomarkers for patients in the data tables of all medical institutions. Furthermore, the server 300 extracts A patients with a high degree of similarity, and calculates the remission rate of the extracted patients for each medical institution (step S923).
  • the server 300 calculates the average remission rate of each medical institution using the remission rates calculated in steps S921 to S923 described above, and uses the calculated average as the recommendation level (step S924). For example, in the example shown in FIG. 18, the average remission rate of medical institution A is 40%, and the average remission rate of medical institution A is 40%.
  • the server 300 recommends a medical institution with a high degree of recommendation to the user (step S925). For example, in the example shown in FIG. 18, the server 300 recommends the medical institution B, which has the highest recommendation degree of 60.
  • a medical institution that has a track record of remission for patients similar to the user can be recommended as a suitable medical institution for the user.
  • the medical institution is recommended to the user, but the present invention is not limited to this.
  • individual specialists may be recommended, or specialists are classified based on attribute information (sex, age, years of treatment experience, number of clinical trials, specialty, etc.), and the class may recommend a specialist included in to the user.
  • FIG. 19 is a block diagram showing an example of hardware configuration.
  • the server 300 will be described below as an example. The same explanation can be given for the user terminal 200 and the medical institution terminal 400 as well. Various types of processing by the server 300 are implemented by cooperation of software and hardware described below.
  • the server 300 has a CPU (Central Processing Unit) 901, a ROM (Read Only Memory) 902, a RAM (Random Access Memory) 903, and a host bus 904a.
  • the server 300 also has a bridge 904 , an external bus 904 b , an interface 905 , an input device 906 , an output device 907 , a storage device 908 , a drive 909 , a connection port 911 and a communication device 913 .
  • the server 300 may have a processing circuit such as a DSP (Digital Signal Processor) or an ASIC (Application Specific Integrated Circuit) in place of or in addition to the CPU 901 .
  • DSP Digital Signal Processor
  • ASIC Application Specific Integrated Circuit
  • the CPU 901 functions as an arithmetic processing device and a control device, and controls overall operations within the server 300 according to various programs.
  • the CPU 901 may be a microprocessor.
  • the ROM 902 stores programs, calculation parameters, and the like used by the CPU 901 .
  • the RAM 903 temporarily stores programs used in the execution of the CPU 901, parameters that change as appropriate during the execution, and the like.
  • the CPU 901 can embody the processing unit 330 of the server 300, for example.
  • the CPU 901, ROM 902 and RAM 903 are interconnected by a host bus 904a including a CPU bus and the like.
  • the host bus 904a is connected via a bridge 904 to an external bus 904b such as a PCI (Peripheral Component Interconnect/Interface) bus.
  • PCI Peripheral Component Interconnect/Interface
  • host bus 904a, bridge 904 and external bus 904b need not necessarily have separate configurations from each other and may be implemented in a single configuration (eg, one bus).
  • the input device 906 is implemented by a device such as a mouse, keyboard, touch panel, button, microphone, switch, lever, etc., through which information is input by the practitioner.
  • the input device 906 may be, for example, a remote control device using infrared rays or other radio waves, or may be an external connection device such as a mobile phone or PDA (Personal Digital Assistant) compatible with the operation of the server 300.
  • the input device 906 may include, for example, an input control circuit that generates an input signal based on information input by the practitioner using the above input means and outputs the signal to the CPU 901 . By operating the input device 906, the practitioner can input various data to the server 300 and instruct processing operations.
  • the output device 907 is formed by a device capable of visually or audibly notifying the practitioner of the acquired information.
  • Such devices include display devices such as CRT (Cathode Ray Tube) display devices, liquid crystal display devices, plasma display devices, EL (Electro Luminescent) display devices and lamps, acoustic output devices such as speakers and headphones, and printer devices. etc.
  • the storage device 908 is a device for storing data.
  • the storage device 908 is realized by, for example, a magnetic storage device such as a HDD (Hard Disk Drive), a semiconductor storage device, an optical storage device, a magneto-optical storage device, or the like.
  • the storage device 908 may include a storage medium, a recording device that records data on the storage medium, a reading device that reads data from the storage medium, a deletion device that deletes data recorded on the storage medium, and the like.
  • the storage device 908 stores programs executed by the CPU 901, various data, and various data acquired from the outside.
  • the storage device 908 can embody the storage unit 360 of the server 300, for example.
  • the drive 909 is a reader/writer for storage media, and is either built into the server 300 or externally attached.
  • the drive 909 reads information recorded on a removable storage medium such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, and outputs the information to the RAM 903 .
  • Drive 909 can also write information to a removable storage medium.
  • connection port 911 is an interface connected to an external device, and is a connection port with an external device capable of data transmission by, for example, USB (Universal Serial Bus).
  • USB Universal Serial Bus
  • the communication device 913 is, for example, a communication interface formed by a communication device or the like for connecting to the network 920 .
  • the communication device 913 is, for example, a communication card for wired or wireless LAN (Local Area Network), LTE (Long Term Evolution), Bluetooth (registered trademark), or WUSB (Wireless USB).
  • the communication device 913 may be a router for optical communication, a router for ADSL (Asymmetric Digital Subscriber Line), a modem for various types of communication, or the like.
  • the communication device 913 can transmit and receive signals to and from the Internet and other communication devices in accordance with a predetermined protocol such as TCP/IP (Transmission Control Protocol/Internet Protocol).
  • the communication device 913 can embody the communication unit 350 of the server 300, for example.
  • the network 920 is a wired or wireless transmission path for information transmitted from devices connected to the network 920 .
  • the network 920 may include a public network such as the Internet, a telephone network, a satellite communication network, various LANs (Local Area Networks) including Ethernet (registered trademark), WANs (Wide Area Networks), and the like.
  • Network 920 may also include a dedicated line network such as IP-VPN (Internet Protocol-Virtual Private Network).
  • the above-described embodiments of the present disclosure include, for example, an information processing method executed by an information processing apparatus or an information processing system as described above, a program for operating the information processing apparatus, and a program in which the program is recorded. may include non-transitory tangible media that have been processed. Also, the program may be distributed via a communication line (including wireless communication) such as the Internet.
  • each step in the information processing method according to the embodiment of the present disclosure described above does not necessarily have to be processed in the described order.
  • each step may be processed in an appropriately changed order.
  • each step may be partially processed in parallel or individually instead of being processed in chronological order.
  • the processing of each step does not necessarily have to be processed in accordance with the described method, and may be processed by another method by another functional unit, for example.
  • each component of each device illustrated is functionally conceptual and does not necessarily need to be physically configured as illustrated.
  • the specific form of distribution and integration of each device is not limited to the one shown in the figure, and all or part of them can be functionally or physically distributed and integrated in arbitrary units according to various loads and usage conditions. Can be integrated and configured.
  • the present technology can also take the following configuration.
  • the information processing device Acquiring sensing data obtained from a sensor attached to a part of a user's body; Extracting the plurality of patients having the sensing data similar to the sensing data of the user from among the plurality of patients based on the sensing data and treatment performance information of the plurality of patients; Determining the medical institution to be recommended to the user from among a plurality of medical institutions based on the extracted information on the treatment results of the plurality of patients; transmitting the determined information of the medical institution to an information processing terminal;
  • a method of processing information comprising: (2) The information processing method according to (1) above, wherein the sensor is a non-invasive sensor device.
  • the non-invasive sensor device is directly attached to a part of the user's body and measures the user's heart rate, pulse, blood flow, blood pressure, perspiration, electroencephalogram, respiration, respiration volume, myoelectric potential, skin temperature,
  • the information processing method according to (2) above wherein at least one of posture, motion state, number of steps, amount of activity, sleep state, sleep time, calorie consumption, facial expression, voice, and line of sight is detected.
  • the information on the treatment results includes information on whether or not the patient is in remission.
  • the information processing device calculating the degree of recommendation of each of the medical institutions based on the extracted information on the treatment results of the plurality of patients; recommending the medical institution with the high recommendation degree to the user;
  • the information processing device calculating a degree of similarity between the sensing data of each patient and the sensing data of the user; Extracting a predetermined number of the patients in descending order of the calculated similarity;
  • the information processing method according to (5) or (6) above comprising: (8) The information processing device Acquiring interview information about mental state from a user terminal used by the user; Acquiring the interview information and the treatment performance information of a plurality of patients from a plurality of medical institutions; calculating a degree of similarity indicating a degree of similarity between the interview information of each patient and the interview information of the user; Extracting a predetermined number of the patients in descending order of the calculated similarity;
  • the information processing method according to (7) above comprising: (9) The information processing device obtaining background information about mental state from a user terminal used by the user; Acquiring the background information and the treatment performance information of a plurality of patients from a plurality of medical institutions; calculating a degree of similarity indicating the degree of similarity between the background information of each patient
  • the information processing device Calculating the consultation rate of the medical institution in the plurality of extracted patients; Setting the calculated consultation rate as the recommendation degree;
  • the information processing method according to any one of (5) to (10) above, including (12) The information processing device Calculating the remission rate by each medical institution in the plurality of extracted patients; setting the calculated remission rate to the recommendation level;
  • the information processing method according to any one of (5) to (10) above, including (13) The information processing device From the plurality of medical institutions, Acquiring at least one of the sensing data, the interview information, and the background information of the plurality of patients; Acquiring information on whether or not the plurality of patients are in remission; Using the acquired information, for each piece of information, calculating the degree of importance indicating the degree of relevance to the information on whether or not remission has occurred;
  • the information processing device is requesting the user terminal to input information based on the calculated importance;
  • an acquisition unit that acquires sensing data obtained from a sensor attached to a part of a user's body, and information on the sensing data and treatment results of a plurality of patients;
  • the plurality of patients having the sensing data similar to the sensing data of the user are extracted from the plurality of patients, and based on the extracted treatment performance information of the plurality of patients, the treatment of the plurality of medical institutions is performed.
  • a recommendation unit that determines the medical institution to recommend to the user from among them;
  • An information processing device An information processing device.
  • the acquisition unit acquires at least one of interview information and background information of the plurality of patients from the plurality of medical institutions, and acquires information as to whether or not the plurality of patients are in remission.
  • the information processing device (10) to the computer, A function of acquiring sensing data obtained from a sensor attached to a part of the user's body; A function of extracting the plurality of patients having the sensing data similar to the sensing data of the user from among the plurality of patients based on the sensing data and treatment performance information of the plurality of patients; a function of determining the medical institution to be recommended to the user from among a plurality of medical institutions based on the extracted information on the treatment performance of the plurality of patients; a function of transmitting information on the determined medical institution to an information processing terminal; The program that causes the to run.
  • An information processing system including a sensor attached to a part of a user's body, an information processing device, and an information processing terminal, The information processing device acquires sensing data obtained from a sensor attached to a part of a user's body, extracting the plurality of patients having the sensing data similar to the sensing data of the user from among the plurality of patients based on the sensing data and treatment performance information of the plurality of patients; determining a medical institution to recommend to the user from among a plurality of medical institutions based on the extracted information on the treatment results of the plurality of patients; transmitting the determined information of the medical institution to the information processing terminal; Information processing system.

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Abstract

Provided is an information processing method, performed by an information processing device (300), comprising: acquiring sensing data obtained from a sensor (140) mounted on a portion of the body of a user; extracting, from among a plurality of patients, a plurality of patients having sensing data similar to the sensing data of the user, on the basis of sensing data and treatment result information of the plurality of patients; determining, on the basis of information about treatment results of the plurality of extracted patients, a medical institution to be recommended to the user, from a plurality of medical institutions; and transmitting, to an information processing terminal (200), information about the determined medical institution.

Description

情報処理方法、情報処理装置、プログラム、及び、情報処理システムInformation processing method, information processing device, program, and information processing system
　本開示は、情報処理方法、情報処理装置、プログラム、及び、情報処理システムに関する。 The present disclosure relates to an information processing method, an information processing device, a program, and an information processing system.
　近年、複数の医療機関の中からより適した医療機関を患者に推薦するための技術が強く求められており、各種の技術が提案されている。例えば、症状の程度等の情報を患者に入力してもらい、これらの情報に基づいて、情報処理システムが患者に好適な医療機関を推薦する。 In recent years, there has been a strong demand for technology to recommend a more suitable medical institution to a patient from among multiple medical institutions, and various technologies have been proposed. For example, the patient is asked to input information such as the degree of symptoms, and based on this information, the information processing system recommends a suitable medical institution for the patient.
特開２０１９－１７９５０１号公報JP 2019-179501 A
　しかしながら、上述のような患者の入力による情報は、客観性の無い情報が含まれていることが多く、このような情報に基づいて、患者に好適な医療機関を推薦することは難しい。また、検査値等の客観的な情報が入力されても、このような情報に基づいて定量的に診断可能な疾患以外の疾患であった場合には、従来の技術では、好適な医療機関を探し出すことは難しいといえる。 However, the information entered by the patient as described above often contains non-objective information, and it is difficult to recommend a suitable medical institution for the patient based on such information. In addition, even if objective information such as test values is input, if the disease is a disease other than a disease that can be quantitatively diagnosed based on such information, the conventional technology cannot find a suitable medical institution. It can be difficult to find.
　そこで、本開示では、患者（ユーザ）に好適な医療機関を推薦することができる情報処理方法、情報処理装置、プログラム、及び、情報処理システムを提案する。 Therefore, the present disclosure proposes an information processing method, an information processing device, a program, and an information processing system capable of recommending a suitable medical institution to a patient (user).
　本開示によれば、情報処理装置が、ユーザの身体に一部に装着されたセンサから得られたセンシングデータを取得することと、複数の患者の前記センシングデータ及び治療実績の情報に基づいて、前記複数の患者の中から、前記ユーザの前記センシングデータと類似する前記センシングデータを持つ前記複数の患者を抽出することと、抽出した前記複数の患者の治療実績の情報に基づいて、複数の医療機関の中から、前記ユーザに推薦する前記医療機関を決定することと、決定された前記医療機関の情報を情報処理端末へ送信することとを含む、情報処理方法が提供される。 According to the present disclosure, an information processing device acquires sensing data obtained from a sensor attached to a part of a user's body, and based on the sensing data and treatment performance information of a plurality of patients, extracting the plurality of patients having the sensing data similar to the sensing data of the user from among the plurality of patients; An information processing method is provided that includes determining the medical institution to be recommended to the user from among the institutions, and transmitting information on the determined medical institution to an information processing terminal.
　また、本開示によれば、ユーザの身体に一部に装着されたセンサから得られたセンシングデータ、及び、複数の患者の前記センシングデータ及び治療実績の情報を取得する取得部と、前記複数の患者の中から、前記ユーザの前記センシングデータと類似する前記センシングデータを持つ前記複数の患者を抽出し、抽出した前記複数の患者の治療実績の情報に基づいて、複数の医療機関の中から、前記ユーザに推薦する前記医療機関を決定する推薦部とを備える、情報処理装置が提供される。 Further, according to the present disclosure, an acquisition unit that acquires sensing data obtained from a sensor attached to a part of a user's body, the sensing data and treatment performance information of a plurality of patients; The plurality of patients having the sensing data similar to the sensing data of the user is extracted from among the patients, and based on the extracted treatment performance information of the plurality of patients, among the plurality of medical institutions, An information processing apparatus is provided, comprising a recommendation unit that determines the medical institution to recommend to the user.
　また、本開示によれば、コンピュータに、ユーザの身体に一部に装着されたセンサから得られたセンシングデータを取得する機能と、複数の患者の前記センシングデータ及び治療実績の情報に基づいて、前記複数の患者の中から、前記ユーザの前記センシングデータと類似する前記センシングデータを持つ前記複数の患者を抽出する機能と、抽出した前記複数の患者の治療実績の情報に基づいて、複数の医療機関の中から、前記ユーザに推薦する前記医療機関を決定する機能と、決定された前記医療機関の情報を情報処理端末に送信する機能とを実行させる、プログラムが提供される。 In addition, according to the present disclosure, the computer has a function of acquiring sensing data obtained from a sensor attached to a part of the user's body, and based on the sensing data and treatment performance information of a plurality of patients, A function of extracting, from among the plurality of patients, the plurality of patients having the sensing data similar to the sensing data of the user; A program is provided for executing a function of determining the medical institution to be recommended to the user from among the institutions and a function of transmitting information on the determined medical institution to an information processing terminal.
　さらに、本開示によれば、ユーザの身体に一部に装着されたセンサと情報処理装置と情報処理端末とを含む情報処理システムであって、前記情報処理装置はユーザの身体に一部に装着されたセンサから得られたセンシングデータを取得し、複数の患者の前記センシングデータ及び治療実績の情報に基づいて、前記複数の患者の中から、前記ユーザの前記センシングデータと類似する前記センシングデータを持つ前記複数の患者を抽出し、抽出した前記複数の患者の治療実績の情報に基づいて、複数の医療機関の中から、前記ユーザに推薦する医療機関を決定し、決定された前記医療機関の情報を前記情報処理端末へ送信する、情報処理システムが提供される。 Furthermore, according to the present disclosure, there is provided an information processing system including a sensor attached to a part of a user's body, an information processing device, and an information processing terminal, wherein the information processing device is attached to a part of the user's body. and obtaining the sensing data obtained from the sensors, and selecting the sensing data similar to the sensing data of the user from among the plurality of patients based on the sensing data and treatment performance information of the plurality of patients. a medical institution to be recommended to the user is determined from among a plurality of medical institutions based on the extracted information on the treatment results of the plurality of patients, and the determined medical institution An information processing system is provided for transmitting information to the information processing terminal.
本開示の実施形態に係る情報処理システム１０の概略的な機能構成を示したシステム図である。1 is a system diagram showing a schematic functional configuration of an information processing system 10 according to an embodiment of the present disclosure; FIG. 本開示の実施形態に係るウェアラブルデバイス１００の機能構成の一例を示すブロック図である。1 is a block diagram showing an example functional configuration of a wearable device 100 according to an embodiment of the present disclosure; FIG. 本開示の実施形態に係るウェアラブルデバイス１００の外観の一例を示す説明図である。1 is an explanatory diagram showing an example of the appearance of a wearable device 100 according to an embodiment of the present disclosure; FIG. 本開示の実施形態に係るユーザ端末２００の機能構成の一例を示すブロック図である。2 is a block diagram showing an example of a functional configuration of a user terminal 200 according to an embodiment of the present disclosure; FIG. 本開示の実施形態に係るサーバ３００の機能構成の一例を示すブロック図である。3 is a block diagram showing an example of a functional configuration of a server 300 according to an embodiment of the present disclosure; FIG. 本開示の実施形態に係る情報処理方法のシーケンス図である。4 is a sequence diagram of an information processing method according to an embodiment of the present disclosure; FIG. 本開示の実施形態に係る医療機関情報テーブル３６２の一例を示す図である。FIG. 10 is a diagram showing an example of a medical institution information table 362 according to an embodiment of the present disclosure; FIG. 本開示の実施形態に係る情報処理方法のフローチャート（その１）である。1 is a flowchart (Part 1) of an information processing method according to an embodiment of the present disclosure; 本開示の実施形態に係るユーザ端末２００の表示の一例を示す説明図である。FIG. 4 is an explanatory diagram showing an example of display on the user terminal 200 according to the embodiment of the present disclosure; FIG. 本開示の実施形態に係る情報処理方法のフローチャート（その２）である。2 is a flowchart (part 2) of an information processing method according to an embodiment of the present disclosure; 本開示の実施形態に係る重要度算出部３３４での情報処理方法を説明するための説明図である。FIG. 13 is an explanatory diagram for explaining an information processing method in the importance calculation unit 334 according to the embodiment of the present disclosure; FIG. 本開示の実施形態に係る情報処理方法のフローチャート（その３）である。3 is a flowchart (part 3) of an information processing method according to an embodiment of the present disclosure; 本開示の実施形態に係る推薦度算出部３３６での情報処理方法を説明するための説明図（その１）である。FIG. 10 is an explanatory diagram (Part 1) for explaining an information processing method in the recommendation degree calculation unit 336 according to the embodiment of the present disclosure; 本開示の実施形態に係る推薦度算出部３３６での情報処理方法を説明するための説明図（その２）である。FIG. 11 is an explanatory diagram (Part 2) for explaining the information processing method in the recommendation degree calculation unit 336 according to the embodiment of the present disclosure; 本開示の実施形態の変形例１に係る情報処理方法のフローチャートである。7 is a flowchart of an information processing method according to Modification 1 of the embodiment of the present disclosure; 本開示の実施形態の変形例１に係る推薦度算出部３３６での情報処理方法を説明するための説明図である。FIG. 13 is an explanatory diagram for explaining an information processing method in the recommendation degree calculation unit 336 according to Modification 1 of the embodiment of the present disclosure; 本開示の実施形態の変形例２に係る情報処理方法のフローチャートである。FIG. 10 is a flowchart of an information processing method according to Modification 2 of the embodiment of the present disclosure; FIG. 本開示の実施形態の変形例２に係る推薦度算出部３３６での情報処理方法を説明するための説明図である。FIG. 11 is an explanatory diagram for explaining an information processing method in a recommendation degree calculation unit 336 according to Modification 2 of the embodiment of the present disclosure; ハードウェア構成の例を示すブロック図である。3 is a block diagram showing an example of hardware configuration; FIG.
　以下に、添付図面を参照しながら、本開示の好適な実施の形態について詳細に説明する。なお、本明細書及び図面において、実質的に同一の機能構成を有する構成要素については、同一の符号を付することにより重複説明を省略する。また、本明細書及び図面において、実質的に同一又は類似の機能構成を有する複数の構成要素を、同一の符号の後に異なるアルファベットを付して区別する場合がある。ただし、実質的に同一又は類似の機能構成を有する複数の構成要素の各々を特に区別する必要がない場合、同一符号のみを付する。 Preferred embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. In the present specification and drawings, constituent elements having substantially the same functional configuration are denoted by the same reference numerals, thereby omitting redundant description. In addition, in this specification and drawings, a plurality of components having substantially the same or similar functional configuration may be distinguished by attaching different alphabets after the same reference numerals. However, when there is no particular need to distinguish between a plurality of components having substantially the same or similar functional configurations, only the same reference numerals are used.
　なお、説明は以下の順序で行うものとする。
１．　本開示の実施形態を創作するに至る背景
２．　本開示の実施形態
　　　２．１　情報処理システム
　　　２．２　ウェアラブルデバイス
　　　２．３　ユーザ端末
　　　２．４　サーバ
　　　２．５　情報処理方法
３．　変形例
　　　３．１　変形例１
　　　３．２　変形例２
４．　まとめ
５．　ハードウェア構成の例
６．　補足 Note that the description will be given in the following order.
1. Background leading to the creation of the embodiments of the present disclosure2. Embodiment of the present disclosure 2.1 Information processing system 2.2 Wearable device 2.3 User terminal 2.4 Server 2.5 Information processing method 3. Modification 3.1 Modification 1
3.2 Modification 2
4. Summary 5. Example of hardware configuration6. supplement
　＜＜１．　本開示の実施形態を創作するに至る背景＞＞
　まずは、本開示の実施形態を説明する前に、本発明者らが本開示の実施形態を創作するに至る背景について説明する。 <<1. Background leading to the creation of the embodiments of the present disclosure >>
First, before describing the embodiments of the present disclosure, the background leading to the creation of the embodiments of the present disclosure by the present inventors will be described.
　先に説明したように、働き盛りの世代において、精神疾患は社会的な問題となっている。精神疾患は、脳機能の障害に起因することが多く、高血圧や糖尿病等とは異なり、定期的、且つ、定量的な検査を行うことで、早期に発見することが難しいと言われている。また、精神疾患は、個々の患者ごとに、原因、症状、及び治療法が異なることから、一貫した治療プロトコルを確立することが難しく、どの医療機関であっても、患者自身に最適な治療を受けられるとは限らない。従って、患者やその家族等が、当該患者に好適な医療機関を探し出すことは難しく、加えて、精神疾患を抱えている患者にとっては、適切、且つ、冷静な判断を行うことが難しい状況にある場合もあることから、やはり好適な医療機関を探し出すことは難しい。 As explained earlier, mental illness has become a social problem in the prime working generation. Psychiatric disorders are often caused by disorders of brain function, and unlike hypertension, diabetes, etc., it is said to be difficult to detect early by conducting regular and quantitative examinations. In addition, it is difficult to establish a consistent treatment protocol for psychiatric disorders because the causes, symptoms, and treatment methods differ for each individual patient. It is not guaranteed that you will receive it. Therefore, it is difficult for patients and their families to find a suitable medical institution for the patient, and in addition, it is difficult for patients with mental illness to make appropriate and calm decisions. Because there are cases, it is still difficult to find a suitable medical institution.
　近年、このような状況を鑑みて、うつ病等の精神疾患の状態を、活動量計などを用いて、定量化しようとする研究が盛んに行われるようになった。さらに、近年、人の精神状態と相関すると考えられる各種の生体情報を、容易に、且つ、日常的に検出することを可能にする様々なセンサが実現されている。 In recent years, in view of this situation, research has been actively conducted to quantify the state of mental illness such as depression using activity meters. Furthermore, in recent years, various sensors have been realized that enable easy and routine detection of various biological information that is considered to be correlated with a person's mental state.
　そこで、このような状況を鑑みて、本発明者らは、このようなセンサによって検出された生体情報を利用して、患者に、精神疾患の治療に好適な医療機関を推薦することができる情報処理方法、情報処理装置、プログラム、及び、情報処理システムに係る本開示の実施形態を創作するに至った。以下、このような本開示の実施形態の詳細を順次説明する。 Therefore, in view of this situation, the present inventors have developed information capable of recommending a suitable medical institution for treatment of mental illness to a patient using biological information detected by such a sensor. Embodiments of the present disclosure related to processing methods, information processing apparatuses, programs, and information processing systems have been created. The details of such embodiments of the present disclosure will be sequentially described below.
　なお、以下の説明において、ユーザとは、本開示の実施形態において医療機関を推薦される対象となる人物のことを意味するものとする。 It should be noted that in the following description, a user means a person for whom a medical institution is recommended in the embodiment of the present disclosure.
　以下の説明において、バイオマーカ（もしくは、デジタルバイオマーカ）とは、非侵襲のセンサデバイスによって日常的に検出可能な、ユーザの精神状態と相関すると考えられている生体情報を意味するものとする。バイオマーカは、例えば、上記センサデバイスによって直接的（詳細には、センサデバイスで取得されたセンシングデータそのもの）、又は、間接的（詳細には、センサデバイスで取得されたセンシングデータそのものを解析処理する）に得られる生体情報である。より具体的には、バイオマーカは、例えば、心拍数、心拍変動、脈拍数、脈拍変動、血流、血中酸素濃度、血圧、呼吸量、呼吸数、脳波、発汗、体温、筋肉状態、姿勢、活動量、運動状態、歩数、走行距離、睡眠時間、睡眠状態（詳細には、レム睡眠、非レム睡眠等）、基礎代謝消費カロリー、運動による消費カロリー、顔や瞳孔の動き等の生体情報であることができる。 In the following description, biomarkers (or digital biomarkers) mean biometric information that can be routinely detected by noninvasive sensor devices and is considered to be correlated with the user's mental state. Biomarkers are, for example, directly by the sensor device (specifically, the sensing data itself acquired by the sensor device), or indirectly (specifically, the sensing data itself acquired by the sensor device). ). More specifically, biomarkers are, for example, heart rate, heart rate variability, pulse rate, pulse variability, blood flow, blood oxygen concentration, blood pressure, respiratory volume, respiratory rate, brain waves, perspiration, body temperature, muscle condition, posture , activity level, exercise state, number of steps, distance traveled, sleep time, sleep state (specifically, REM sleep, non-REM sleep, etc.), basal metabolic calorie consumption, calorie consumption due to exercise, biological information such as facial and pupil movements can be
　また、以下の説明において、問診情報は、精神疾患の患者に対して専門医等から診断のために聴取する情報全般のことを意味する。具体的には、問診情報は、例えば、年齢、性別、職業、喫煙歴、飲酒の度合い、活動時間、勤務時間、食事内容、家族構成、嗜好、趣味、生育履歴等の情報である。 In addition, in the following explanation, interview information means all the information collected from specialists, etc. for diagnosis of patients with mental illness. Specifically, the interview information includes information such as age, gender, occupation, smoking history, degree of drinking, activity hours, working hours, meal content, family structure, preferences, hobbies, and growth history.
　さらに、以下の説明において、精神状態に関する患者の背景情報（以下、スコアとも呼ぶ場合がある）は、精神疾患の患者に対して専門医等から診断のために聴取する主観評価結果を意味する。具体的には、背景情報は、例えば、ＰＨＱ－９（Ｐａｔｉｅｎｔ　Ｈｅａｌｔｈ　Ｑｕｅｓｔｉｏｎｎａｉｒｅ－９）、ＧＡＤ－７（Ｇｅｎｅｒａｌ　Ａｎｘｉｅｔｙ　Ｄｉｓｏｒｄｅｒ－７）等といった精神疾患の診断として一般的に用いられている、複数の質問に対する患者の主観的評価をスコア化した結果を意味する。 Furthermore, in the following explanation, the patient's background information on mental status (hereinafter also referred to as score) means the subjective evaluation results obtained from specialists for diagnosis of patients with mental illness. Specifically, the background information is, for example, PHQ-9 (Patient Health Questionnaire-9), GAD-7 (General Anxiety Disorder-7), etc. It means the result of scoring the patient's subjective evaluation of
　さらに、以下の説明において、治療実績とは、例えば、患者が寛解したか否かの情報や、寛解までの治療期間、治療方法、投薬情報、担当医療機関、担当医師等の情報を意味するものとする。 Furthermore, in the following explanation, the treatment results mean, for example, information as to whether or not the patient is in remission, treatment period until remission, treatment method, medication information, medical institution in charge, doctor in charge, etc. and
　以下、精神疾患の治療のための医療機関をユーザに推薦する実施形態について説明するが、本実施形態は、このような実施形態に限定されるものではなく、他の身体疾患の治療のための医療機関をユーザに推薦する際にも適用することが可能である。 Hereinafter, an embodiment for recommending a user a medical institution for treatment of mental illness will be described, but this embodiment is not limited to such an embodiment, and other medical institutions for treatment of other physical illnesses will be described. It can also be applied when recommending a medical institution to a user.
　＜＜２．　本開示の実施形態＞＞
　＜２．１　情報処理システム＞
　まずは、本開示の実施形態に係る情報処理システム１０の構成の一例について、図１を参照して説明する。図１は、本開示の実施形態に係る情報処理システム１０の概略的な機能構成を示したシステム図である。 <<2. Embodiment of the Present Disclosure>>
<2.1 Information processing system>
First, an example configuration of an information processing system 10 according to an embodiment of the present disclosure will be described with reference to FIG. FIG. 1 is a system diagram showing a schematic functional configuration of an information processing system 10 according to an embodiment of the present disclosure.
　図１に示すように、本実施形態に係る情報処理システム１０は、ウェアラブルデバイス１００と通信可能に接続されるユーザ端末（情報処理端末）２００と、サーバ（情報処理装置）３００と、医療機関端末４００とを主に含み、これらは互いにネットワーク５００を介して通信可能に接続される。詳細には、ユーザ端末２００、サーバ３００及び医療機関端末４００は、図示しない基地局等（例えば、携帯電話機の基地局、無線ＬＡＮ（Ｌｏｃａｌ　Ａｒｅａ　ｎｅｔｗｏｒｋ）のアクセスポイント等）を介してネットワーク５００に接続される。なお、ネットワーク５００で用いられる通信方式は、有線又は無線（例えば、ＷｉＦｉ（登録商標）、Ｂｌｕｅｔｏｏｔｈ（登録商標）等）を問わず任意の方式を適用することができるが、安定した動作を維持することができる通信方式を用いることが望ましい。以下に、本実施形態に係る情報処理システム１０の含まれる各装置の概要について説明する。 As shown in FIG. 1, an information processing system 10 according to the present embodiment includes a user terminal (information processing terminal) 200 communicably connected to a wearable device 100, a server (information processing device) 300, and a medical institution terminal. 400 , which are communicably connected to each other via a network 500 . Specifically, the user terminal 200, the server 300, and the medical institution terminal 400 are connected to the network 500 via a base station (for example, a mobile phone base station, a wireless LAN (Local Area network) access point, etc.) (not shown). be done. Note that the communication method used in the network 500 can be any method regardless of whether it is wired or wireless (for example, WiFi (registered trademark), Bluetooth (registered trademark), etc.), but stable operation is maintained. It is desirable to use a communication method that can Below, an outline of each device included in the information processing system 10 according to the present embodiment will be described.
　（ウェアラブルデバイス１００）
　ウェアラブルデバイス１００は、ユーザの身体の一部（顔、耳たぶ、首、腕、手首、足首等）に装着可能なデバイスであることができる。より具体的には、ウェアラブルデバイス１００は、ＨＭＤ（Ｈｅａｄ　Ｍｏｕｎｔｅｄ　Ｄｉｓｐｌａｙ）型、眼鏡型、イヤーデバイス型、アンクレット型、腕輪（リストバンド）型、首輪型、アイウェア型、パッド型、バッチ型、衣服型、帽子型、マスク型等の各種の方式のウェアラブルデバイスであることができる。 (Wearable device 100)
Wearable device 100 can be a device that can be worn on a user's body part (face, earlobe, neck, arm, wrist, ankle, etc.). More specifically, the wearable device 100 is a head mounted display (HMD) type, eyeglass type, ear device type, anklet type, bracelet (wristband) type, collar type, eyewear type, pad type, batch type, clothes It can be a wearable device of various types such as a type, a hat type, and a mask type.
　さらに、ウェアラブルデバイス１００は、ユーザのバイオマーカを取得することが可能な、非侵襲のセンサデバイスであるセンサ部１４０（図２　参照）を有する。センサ部１４０には、例えば、ユーザの脈拍、心拍、血流、欠中酸素等を検出する血流センサ、ユーザの心電図を検出するＥＣＧ（Ｅｌｅｃｔｒｏｃａｒｄｉｏｇｒａｍ）センサ、ユーザの血圧を検出する血圧センサ、ユーザの発汗を検出する発汗センサ、ユーザの脳波（加えて、脳波からユーザの睡眠状態やリラックス等の状態を間接的に検出可能）を検出する脳波センサ、ユーザの体温を検出する体温センサ、ユーザの筋肉の緊張状態を検出する筋電位センサ、ユーザの呼吸数や呼吸量を検出する呼吸センサ等が設けられていてもよい。 Furthermore, the wearable device 100 has a sensor section 140 (see FIG. 2), which is a non-invasive sensor device capable of acquiring the user's biomarkers. The sensor unit 140 includes, for example, a blood flow sensor that detects the user's pulse, heart rate, blood flow, intermittent oxygen, etc., an ECG (Electrocardiogram) sensor that detects the user's electrocardiogram, a blood pressure sensor that detects the user's blood pressure, a user A perspiration sensor that detects the perspiration of the user, an electroencephalogram sensor that detects the user's electroencephalogram (and can indirectly detect the user's state of sleep, relaxation, etc. from the electroencephalogram), a body temperature sensor that detects the user's body temperature, a user's A myoelectric potential sensor that detects the tension state of muscles, a respiration sensor that detects the user's respiration rate and respiration volume, and the like may be provided.
　また、センサ部１４０は、ユーザの姿勢や動き、すなわち運動状態や活動状態を検出するためのモーションセンサを含んでもよい。モーションセンサは、例えば、ユーザの動作に伴って発生する加速度の変化を示すセンシングデータを取得し、必要に応じて解析処理することにより、当該ユーザの運動状態、姿勢、歩数、走行距離、活動量、睡眠状態、睡眠時間、消費エネルギー、基礎代謝消費エネルギー等を検出することができる。また、本実施形態においては、センサ部１４０は、ユーザの表情、顔、視線の動き、瞳孔の動きを捉える撮像装置（図示省略）や、ユーザの音声を取得するマイクロフォン（以下、マイクと称する）（図示省略）等を含んでもよい。 The sensor unit 140 may also include a motion sensor for detecting the user's posture and movement, that is, the state of exercise and the state of activity. The motion sensor, for example, acquires sensing data indicating changes in acceleration that occur with the motion of the user, and performs analysis processing as necessary to determine the user's exercise state, posture, number of steps, distance traveled, and amount of activity. , sleep state, sleep time, energy consumption, basal metabolic energy consumption, and the like can be detected. In addition, in the present embodiment, the sensor unit 140 includes an imaging device (not shown) that captures the user's expression, face, line of sight, and pupil movement, and a microphone that acquires the user's voice (hereinafter referred to as a microphone). (illustration omitted) etc. may be included.
　なお、以下の説明においては、ウェアラブルデバイス１００は、例えば、腕輪（リストバンド）型ウェアラブルデバイスであるものとする。また、ウェアラブルデバイス１００の詳細については後述する。 In the following description, the wearable device 100 is assumed to be, for example, a bracelet (wristband) type wearable device. Details of the wearable device 100 will be described later.
　（ユーザ端末２００）
　ユーザ端末２００は、ユーザが日常的に使用する端末であって、ウェアラブルデバイス１００からのバイオマーカを後述するサーバ３００へ送信することができ、サーバ３００からの情報を受信することができる。例えば、ユーザ端末２００は、タブレット、スマートフォン、携帯電話、ラップトップ型ＰＣ（Ｐｅｒｓｏｎａｌ　Ｃｏｍｐｕｔｅｒ）、デスクトップ型ＰＣ、Ｈｅａｄ　Ｍｏｕｎｔｅｄ　Ｄｉｓｐｌａｙ（ＨＭＤ）等のデバイスであることができる。なお、以下の説明においては、ユーザ端末２００は、スマートフォンであるものとする。また、ユーザ端末２００の詳細については後述する。 (User terminal 200)
The user terminal 200 is a terminal that the user uses on a daily basis, and is capable of transmitting biomarkers from the wearable device 100 to the server 300 described below and receiving information from the server 300 . For example, the user terminal 200 can be a device such as a tablet, a smart phone, a mobile phone, a laptop PC (Personal Computer), a desktop PC, or a Head Mounted Display (HMD). In addition, in the following description, the user terminal 200 shall be a smart phone. Details of the user terminal 200 will be described later.
　（サーバ３００）
　サーバ３００は、ウェアラブルデバイス１００からユーザ端末２００を介して取得したバイオマーカ、及び、ユーザがユーザ端末２００を用いて入力した情報等に基づき、当該ユーザに推薦する医療機関等を決定し、決定した情報をユーザに提供することができる。サーバ３００は、例えば、コンピュータ等により構成される。なお、サーバ３００の詳細構成については後述する。 (Server 300)
Based on the biomarkers obtained from the wearable device 100 via the user terminal 200 and the information input by the user using the user terminal 200, the server 300 determines and determines the medical institution or the like to be recommended to the user. Information can be provided to the user. The server 300 is configured by, for example, a computer. A detailed configuration of the server 300 will be described later.
　（医療機関端末４００）
　医療機関端末４００は、医師等の医療従事者が医療機関において使用する端末であって、ネットワーク５００を介してサーバ３００へ情報を送信することができる。例えば、医療機関端末４００は、タブレット、スマートフォン、携帯電話、ラップトップ型ＰＣ、デスクトップ型ＰＣ等のデバイスであることができる。なお、以下の説明においては、医療機関端末４００は、デスクトップ型ＰＣであるものとする。また、医療機関端末４００の詳細については後述する。 (Medical institution terminal 400)
The medical institution terminal 400 is a terminal used by medical personnel such as doctors at the medical institution, and can transmit information to the server 300 via the network 500 . For example, the medical institution terminal 400 can be a device such as a tablet, smart phone, mobile phone, laptop PC, or desktop PC. In the following description, medical institution terminal 400 is assumed to be a desktop PC. Details of the medical institution terminal 400 will be described later.
　なお、図１においては、本実施形態に係る情報処理システム１０は、１対のウェアラブルデバイス１００とユーザ端末２００を含むものとして示されているが、本実施形態においてはこれに限定されるものではない。例えば、本実施形態に係る情報処理システム１０は、複数のウェアラブルデバイス１００と１つ又は複数のユーザ端末２００の対を１つ又は複数含んでいてもよい。さらに、本実施形態においては、情報処理システム１０は、複数の医療機関端末４００を含んでいてもよい。さらに、本実施形態に係る情報処理システム１０は、例えば、複数のユーザ端末２００及び複数の医療機関端末４００と、サーバ３００との間で情報の送受信するための中継装置のような他の通信装置等を含んでもよい。 Although FIG. 1 shows the information processing system 10 according to the present embodiment as including a pair of wearable device 100 and user terminal 200, the present embodiment is not limited to this. do not have. For example, the information processing system 10 according to the present embodiment may include one or more pairs of multiple wearable devices 100 and one or multiple user terminals 200 . Furthermore, in this embodiment, the information processing system 10 may include a plurality of medical institution terminals 400 . Furthermore, the information processing system 10 according to the present embodiment includes other communication devices such as relay devices for transmitting and receiving information between the plurality of user terminals 200 and the plurality of medical institution terminals 400 and the server 300, for example. etc. may be included.
　＜２．２　ウェアラブルデバイス＞
　次に、本実施形態に係るウェアラブルデバイス１００の詳細構成について、図２を参照して説明する。図２は、本実施形態に係るウェアラブルデバイス１００の機能構成の一例を示すブロック図である。 <2.2 Wearable device>
Next, the detailed configuration of the wearable device 100 according to this embodiment will be described with reference to FIG. FIG. 2 is a block diagram showing an example of the functional configuration of the wearable device 100 according to this embodiment.
　ウェアラブルデバイス１００は、図２に示すように、入力部１１０と、出力部１２０と、制御部１３０と、センサ部１４０と、通信部１５０と、記憶部１６０とを主に有する。以下に、ウェアラブルデバイス１００の各機能部の詳細について説明する。 The wearable device 100 mainly has an input unit 110, an output unit 120, a control unit 130, a sensor unit 140, a communication unit 150, and a storage unit 160, as shown in FIG. Details of each functional unit of the wearable device 100 will be described below.
　（入力部１１０）
　入力部１１０は、ウェアラブルデバイス１００へのユーザからのデータ、コマンドの入力を受け付ける。より具体的には、当該入力部１１０は、タッチパネル、ボタン、マイク等により実現される。 (Input unit 110)
The input unit 110 receives input of data and commands from the user to the wearable device 100 . More specifically, the input unit 110 is implemented by a touch panel, buttons, a microphone, and the like.
　（出力部１２０）
　出力部１２０は、ユーザに対して情報を提示するためのデバイスであり、例えば、ユーザに向けて、画像、音声、光、又は、振動等により各種の情報を出力する。より具体的には、出力部１２０は、後述するサーバ３００から提供される情報を画面表示したりすることができる。当該出力部１２０は、ディスプレイ、スピーカ、イヤフォン、発光素子（例えば、Ｌｉｇｈｔ　Ｅｍｉｔｔｉｎｇ　Ｄｉｏｄｅ（ＬＥＤ））、振動モジュール等により実現される。なお、出力部１２０の機能の一部は、ユーザ端末２００により提供されてもよい。 (Output unit 120)
The output unit 120 is a device for presenting information to the user, and for example, outputs various information to the user using images, sounds, lights, vibrations, or the like. More specifically, the output unit 120 can display information provided from the server 300, which will be described later, on the screen. The output unit 120 is implemented by a display, a speaker, earphones, a light-emitting element (for example, a Light Emitting Diode (LED)), a vibration module, and the like. Note that part of the functions of the output unit 120 may be provided by the user terminal 200 .
　（制御部１３０）
　制御部１３０は、ウェアラブルデバイス１００内に設けられ、ウェアラブルデバイス１００の各機能部を制御したり、後述するセンサ部１４０から出力されたセンシングデータ（バイオマーカ）を取得したり、当該センシングデータを解析処理したりすることができる。当該制御部１３０は、例えば、ＣＰＵ（Ｃｅｎｔｒａｌ　Ｐｒｏｃｅｓｓｉｎｇ　Ｕｎｉｔ）、ＲＯＭ（Ｒｅａｄ　Ｏｎｌｙ　Ｍｅｍｏｒｙ）、ＲＡＭ（Ｒａｎｄｏｍ　Ａｃｃｅｓｓ　Ｍｅｍｏｒｙ）等のハードウェアにより実現される。なお、制御部１３０の機能の一部は、後述するユーザ端末２００により提供されてもよい。 (control unit 130)
The control unit 130 is provided in the wearable device 100, controls each functional unit of the wearable device 100, acquires sensing data (biomarkers) output from the sensor unit 140 described later, and analyzes the sensing data. can be processed. The control unit 130 is realized by hardware such as a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), and the like. Note that part of the functions of the control unit 130 may be provided by the user terminal 200, which will be described later.
　（センサ部１４０）
　センサ部１４０は、ユーザの身体に装着されたウェアラブルデバイス１００内に設けられ、ユーザの生体情報を検出する各種センサを有する。詳細には、当該センサ部１４０は、例えば、ユーザの脈拍又は心拍を検出して、心拍又は脈拍の時系列データを取得するＰＰＧセンサ（拍動センサ）（図示省略）や、ユーザの動きを検出するモーションセンサ（図示省略）等を有する。 (Sensor unit 140)
The sensor unit 140 is provided in the wearable device 100 attached to the user's body, and has various sensors that detect the user's biological information. Specifically, the sensor unit 140 is, for example, a PPG sensor (pulse sensor) (not shown) that detects the pulse or heartbeat of the user and acquires time-series data of the heartbeat or pulse, or a sensor that detects the movement of the user. It has a motion sensor (not shown) and the like.
　具体的には、ＰＰＧセンサは、ユーザの脈波信号を検出するために、ユーザの皮膚等の身体の一部（例えば、両腕、手首、足首等）に装着される生体センサである。ここで、脈波信号とは、心臓の筋肉が一定のリズムで収縮すること（拍動、なお、単位時間の心臓における拍動回数を心拍数と呼ぶ）により、動脈を通じ全身に血液が送られることにより、動脈内壁に圧力の変化が生じ、体表面等に現れる動脈の拍動による波形のことである。ＰＰＧセンサは、脈波信号を取得するために、手や腕、脚等のユーザの測定部位内の血管に光を照射し、対象ユーザの血管中を移動する物質や静止している生体組織で散乱された光を検出する。照射した光は血管中の赤血球により吸収されることから、光の吸収量は、測定部位内の血管に流れる血液量に比例する。従って、ＰＰＧセンサは、散乱された光の強度を検出することにより流れる血液量の変化を知ることができる。さらに、この血流量の変化から、拍動の波形、すなわち、脈波信号を検出することができる。なお、このような方法は、光電容積脈波（ＰｈｏｔｏＰｌｅｔｈｙｓｍｏＧｒａｐｈｙ；ＰＰＧ）法と呼ばれる。 Specifically, the PPG sensor is a biosensor worn on a part of the body such as the user's skin (for example, both arms, wrists, ankles, etc.) in order to detect the user's pulse wave signal. Here, the pulse wave signal refers to the contraction of the heart muscle at a constant rhythm (pulsation, the number of heart beats per unit time is called the heart rate), and blood is sent throughout the body through the arteries. As a result, a change in pressure occurs on the inner wall of the artery, and it is a waveform due to the pulsation of the artery that appears on the body surface. In order to acquire a pulse wave signal, the PPG sensor irradiates light on the blood vessels in the measurement site of the user, such as a hand, arm, leg, etc. Detect scattered light. Since the irradiated light is absorbed by red blood cells in the blood vessel, the amount of light absorption is proportional to the amount of blood flowing through the blood vessel in the measurement site. Therefore, the PPG sensor can detect changes in the amount of flowing blood by detecting the intensity of the scattered light. Furthermore, a pulsation waveform, that is, a pulse wave signal can be detected from this change in blood flow. Such a method is called PhotoPlethysmoGraphy (PPG) method.
　詳細には、ＰＰＧセンサは、コヒーレント光を照射することができる、小型レーザやＬＥＤ（Ｌｉｇｈｔ　Ｅｍｉｔｔｉｎｇ　Ｄｉｏｄｅ）（図示省略）等を内蔵し、例えば８５０ｎｍ前後のような所定の波長を持つ光を照射する。なお、本実施形態においては、ＰＰＧが照射する光の波長は、適宜選択することが可能である。さらに、ＰＰＧセンサは、例えばフォトダイオード（Ｐｈｏｔｏ　Ｄｅｔｅｃｔｏｒ：ＰＤ）を内蔵し、検出した光の強度を電気信号に変換することにより、脈波信号を取得する。なお、ＰＰＧセンサは、ＰＤの代わりに、ＣＣＤ（Ｃｈａｒｇｅ　Ｃｏｕｐｌｅｄ　Ｄｅｖｉｃｅｓ）型センサ、ＣＭＯＳ（Ｃｏｍｐｌｅｍｅｎｔａｒｙ　Ｍｅｔａｌ　Ｏｘｉｄｅ　Ｓｅｍｉｃｏｎｄｕｃｔｏｒ）型センサ等を内蔵してもよい。また、ＰＰＧセンサには、ユーザの測定部位からの光を検出するために、レンズやフィルタ等の光学系機構が含まれていてもよい。 Specifically, the PPG sensor incorporates a small laser or LED (Light Emitting Diode) (not shown) that can irradiate coherent light, and irradiates light with a predetermined wavelength such as around 850 nm, for example. . In addition, in this embodiment, the wavelength of the light emitted by the PPG can be selected as appropriate. Furthermore, the PPG sensor incorporates, for example, a photodiode (Photo Detector: PD), and acquires a pulse wave signal by converting the intensity of the detected light into an electrical signal. The PPG sensor may incorporate a CCD (Charge Coupled Devices) type sensor, a CMOS (Complementary Metal Oxide Semiconductor) type sensor, or the like instead of the PD. The PPG sensor may also include optical mechanisms such as lenses and filters to detect light from the user's measurement site.
　そして、ＰＰＧセンサにより、複数のピークを有する時系列データとして脈波信号を検出することができる。脈波信号に現れる複数のピークのピーク間隔を、脈拍間隔（Ｐｕｌｓｅ　Ｒａｔｅ　Ｉｎｔｅｒｖａｌ：ＰＰＩ）と呼ぶ。ＰＰＩの値は、ＰＰＧセンサにより検出された脈波信号を処理することにより、取得することができる。各ＰＰＩの値は、時間とともに変動するものの、ユーザの状態が一定に維持されている間においては、ほぼ正規分布することが知られている。そこで、ＰＰＩ値のデータ群を統計的に処理する（例えば、ＰＰＩ値の標準偏差を算出する）ことにより、ユーザの精神状態（例えば、ストレ氏状態）の指標となる各種ＨＲＶ（Ｈｅａｒｔ　Ｒａｔｅ　Ｖａｒｉａｂｉｌｉｔｙ）指標を算出することができる。従って、心拍又は脈拍の時系列データは、ユーザの精神状態を示す１つの指標となり得る。 Then, the PPG sensor can detect the pulse wave signal as time-series data having multiple peaks. A peak interval between a plurality of peaks appearing in a pulse wave signal is called a pulse rate interval (PPI). The PPI value can be obtained by processing the pulse wave signal detected by the PPG sensor. It is known that each PPI value fluctuates over time, but is approximately normally distributed while the user's state is maintained constant. Therefore, by statistically processing the PPI value data group (for example, calculating the standard deviation of the PPI value), various HRV (Heart Rate Variability) indicators of the user's mental state (for example, Stre's state) An index can be calculated. Therefore, time-series data of heartbeat or pulse can serve as an indicator of the user's mental state.
　本実施形態においては、上述のＰＰＧ法を利用して脈波信号を取得することに限定されるものではなく、他の方法によっても脈波信号を取得してもよい。例えば、本実施形態においては、センサ部１４０は、レーザドップラー血流計測法を用いて脈波を検出してもよい。当該レーザドップラー血流計測法は、以下のような現象を利用して血流を測定する方法である。詳細には、レーザ光をユーザの測定部位に照射した際には、当該ユーザの血管内に存在する散乱物質（主に赤血球）が移動していることにより、ドップラーシフトを伴った散乱光が生じる。そして、当該ドップラーシフトを伴った散乱光が、ユーザの測定部位に存在する、移動しない組織による散乱光と干渉し、ビート状の強度変化が観測されることとなる。そこで、当該レーザドップラー血流計測法は、ビート信号の強度と周波数を解析することにより、血流を測定することができる。 The present embodiment is not limited to obtaining pulse wave signals using the PPG method described above, and pulse wave signals may be obtained by other methods. For example, in the present embodiment, the sensor unit 140 may detect pulse waves using a laser Doppler blood flow measurement method. The laser Doppler blood flow measurement method is a method of measuring blood flow using the following phenomena. Specifically, when the user's measurement site is irradiated with laser light, scattering substances (mainly red blood cells) present in the user's blood vessels move, causing scattered light with a Doppler shift. . Then, the scattered light accompanied by the Doppler shift interferes with the scattered light from non-moving tissue present in the measurement site of the user, and a beat-like intensity change is observed. Therefore, the laser Doppler blood flow measurement method can measure blood flow by analyzing the intensity and frequency of the beat signal.
　また、本実施形態においては、先に説明したように、ユーザの身体に貼り付けられた電極（図示省略）を介して当該対象ユーザの心電図を検出するＥＣＧセンサが設けられていてもよい。この場合、検出した心電図から、心臓の拍動間隔であるＲ－Ｒ間隔（ＲＲＩ）を取得し、ＲＲＩ値の時系列データからユーザの精神状態を示す指標であるＨＲＶ指標を算出することができる。 Also, in this embodiment, as described above, an ECG sensor that detects the electrocardiogram of the target user via electrodes (not shown) attached to the user's body may be provided. In this case, the RR interval (RRI), which is the interval between heart beats, is acquired from the detected electrocardiogram, and the HRV index, which is an index indicating the mental state of the user, can be calculated from the time-series data of the RRI value. .
　また、本実施形態においては、センサ部１４０には、発汗センサ（図示省略）が設けられていてもよい。詳細には、人間に生じる発汗には、温熱性発汗と精神性発汗との主に２種類の発汗があると一般的にいわれている。温熱性発汗は、体温調節のために行われる発汗である。一方、精神性発汗は、緊張や喜怒哀楽等の人間の情動によって生じる発汗であり、常温において、掌や足裏等上で、温熱性発汗に比べて瞬時、且つ、微量に生じる発汗である。例えば、精神性発汗は、プレゼンテーションを行う際に緊張によって生じる発汗等のことである。このような精神性発汗は、交感神経優位時に多く出ることが知られており、精神状態を示す指標になり得ると一般的に考えられている。そこで、上記発汗センサは、ユーザの皮膚に装着され、発汗により変化する当該皮膚上の２点間の電圧又は抵抗を検出する。そして、本実施形態においては、発汗センサによって検出されたセンシングデータに基づいて、発汗量、発汗頻度、発汗量の変化等の情報を取得することにより、ユーザの精神状態を示す１つの指標を得ることができる。 Also, in the present embodiment, the sensor unit 140 may be provided with a perspiration sensor (not shown). Specifically, it is generally said that there are mainly two types of perspiration that occur in humans: thermal perspiration and mental perspiration. Thermal sweating is sweating that is done to regulate body temperature. On the other hand, mental sweating is sweating caused by human emotions such as tension and emotions. It is sweating that occurs instantaneously and in a small amount on the palms and soles of the feet at room temperature compared to thermal sweating. . For example, mental sweating is sweating caused by tension when giving a presentation. Such mental sweating is known to occur frequently when the sympathetic nervous system is dominant, and is generally considered to be an indicator of mental state. Therefore, the perspiration sensor is attached to the user's skin and detects the voltage or resistance between two points on the skin that changes due to perspiration. In this embodiment, information such as the amount of perspiration, the frequency of perspiration, and changes in the amount of perspiration is obtained based on the sensing data detected by the perspiration sensor, thereby obtaining one index indicating the mental state of the user. be able to.
　また、本実施形態においては、センサ部１４０は、他の各種生体センサ（図示省略）を含んでいてもよい。例えば、当該各種生体センサは、ユーザの身体の一部に直接的又は間接的に装着され、対象ユーザの脳波、呼吸、筋電位、皮膚温度等を測定する１つ又は複数のセンサを含むことができる。より具体的には、本実施形態においては、例えば、ユーザの脳波を測定する脳波センサ部（図示省略）により得られたセンシングデータを解析して、当該脳波の種類（例えば、α波、β波等の種類）を判定することにより、ユーザの精神状態（例えば、ユーザのリラックスの度合い等）を示す指標を得ることができる。 Also, in the present embodiment, the sensor unit 140 may include other various biosensors (not shown). For example, the various biosensors may include one or more sensors attached directly or indirectly to a part of the user's body and measuring the target user's electroencephalogram, respiration, myoelectric potential, skin temperature, etc. can. More specifically, in the present embodiment, for example, sensing data obtained by an electroencephalogram sensor unit (not shown) that measures the electroencephalogram of the user is analyzed to determine the type of electroencephalogram (e.g., alpha waves, beta waves, etc.), it is possible to obtain an index indicating the mental state of the user (for example, the degree of relaxation of the user, etc.).
　さらに、本実施形態においては、センサ部１４０は、先に説明したように、ユーザの表情を捉える撮像装置（図示省略）を含んでいてもよい。この場合、当該撮像装置は、例えば、ユーザの眼球運動、瞳孔径の大きさ、凝視時間等を検出する。人間の瞳孔径を支配する筋は、交感神経／副交感神経から影響を受けているといわれている。従って、本実施形態においては、上記撮像装置によりユーザの瞳孔径を検知することにより、当該ユーザの交感神経／副交感神経の状態等、すなわち、ユーザの精神状態を示す１つの指標を得ることができる。 Furthermore, in this embodiment, the sensor unit 140 may include an imaging device (not shown) that captures the user's facial expression, as described above. In this case, the imaging device detects, for example, the user's eye movement, pupil diameter, gaze time, and the like. It is said that the muscle that controls the human pupil diameter is influenced by the sympathetic/parasympathetic nerves. Therefore, in this embodiment, by detecting the user's pupil diameter with the imaging device, it is possible to obtain one index indicating the user's mental state, such as the user's sympathetic/parasympathetic state. .
　また、センサ部１４０は、ユーザの姿勢、動き、すなわち運動状態を検出するためのモーションセンサを含んでもよい。モーションセンサは、ユーザの動作に伴って発生する加速度の変化を示すセンシングデータを取得することにより、当該ユーザの運動状態を検出する。当該モーションセンサにより取得されたユーザの運動状態は、当該ユーザの情動を推定する際に用いることができる。また、姿勢は呼吸の深さ等に影響を与えると言われ、さらに、呼吸の深さは、人の緊張状態（緊張度）と関連性が高いと言われている。そこで、本実施形態においては、ユーザの姿勢を検出し、検出した姿勢からユーザの精神状態を示す１つの指標を得ることができる。具体的には、モーションセンサは、加速度センサ、ジャイロセンサ、地磁気センサ等（図示省略）を含む。 The sensor unit 140 may also include a motion sensor for detecting the user's posture and movement, that is, the motion state. The motion sensor detects the motion state of the user by acquiring sensing data indicating changes in acceleration that occur with the motion of the user. The user's exercise state acquired by the motion sensor can be used when estimating the user's emotion. In addition, it is said that the posture affects the depth of breathing and the like, and furthermore, the depth of breathing is said to be highly related to the state of tension (degree of tension) of a person. Therefore, in the present embodiment, it is possible to detect the user's posture and obtain one index indicating the user's mental state from the detected posture. Specifically, the motion sensor includes an acceleration sensor, a gyro sensor, a geomagnetic sensor, etc. (not shown).
　また、当該モーションセンサは、ユーザを撮像する撮像装置（図示省略）であってもよい。この場合、上記撮像装置によって撮像される画像によって、当該ユーザの動作等がキャプチャすることが可能であることから、当該撮像装置により、ユーザの運動状態を検出することができる。さらに、当該モーションセンサは、ユーザの動作を検出することができる赤外線センサ、超音波センサ等（図示省略）を含んでいてもよい。なお、このような撮像装置及び赤外線センサ等は、ユーザの周囲に、ウェアラブルデバイス１００とは別体の装置として設置されていてもよい。 Also, the motion sensor may be an imaging device (not shown) that captures an image of the user. In this case, since the motion of the user can be captured by the image captured by the imaging device, the user's motion state can be detected by the imaging device. Further, the motion sensor may include an infrared sensor, an ultrasonic sensor, or the like (not shown) capable of detecting user motion. Note that such an imaging device, an infrared sensor, and the like may be installed around the user as separate devices from the wearable device 100 .
　また、センサ部１４０は、モーションセンサと共に、測位センサ（図示省略）を含んでいてもよい。当該測位センサは、ウェアラブルデバイス１００を装着したユーザの位置を検出するセンサであり、具体的には、ＧＮＳＳ（Ｇｌｏｂａｌ　Ｎａｖｉｇａｔｉｏｎ　Ｓａｔｅｌｌｉｔｅ　Ｓｙｓｔｅｍ）受信機等であることができる。この場合、測位センサは、ＧＮＳＳ衛星からの信号に基づいて、ユーザの現在地の緯度・経度を示すセンシングデータを生成することができる。また、本実施形態においては、例えば、ＲＦＩＤ（Ｒａｄｉｏ　Ｆｒｅｑｕｅｎｃｙ　Ｉｄｅｎｔｉｆｉｃａｔｉｏｎ）、Ｗｉ－Ｆｉのアクセスポイント、無線基地局の情報等からユーザの相対的な位置関係を検出することが可能なため、このような通信装置を上記測位センサとして利用することも可能である。 Also, the sensor unit 140 may include a positioning sensor (not shown) together with the motion sensor. The positioning sensor is a sensor that detects the position of the user wearing the wearable device 100, and specifically can be a GNSS (Global Navigation Satellite System) receiver or the like. In this case, the positioning sensor can generate sensing data indicating the latitude and longitude of the user's current location based on signals from GNSS satellites. In addition, in the present embodiment, for example, RFID (Radio Frequency Identification), Wi-Fi access point, since it is possible to detect the relative positional relationship of the user from the information of the wireless base station, such It is also possible to use a communication device as the positioning sensor.
　そして、センサ部１４０は、ユーザの発話音声を検出するマイク（図示省略）を含んでいてもよい。例えば、本実施形態においては、当該マイクから検出された音から特定の音声（例えば、ユーザが発話した特定の文言）を抽出することによって得られた結果を、ユーザの精神状態を示す１つの指標として取得してもよい。 The sensor unit 140 may include a microphone (not shown) that detects the user's uttered voice. For example, in the present embodiment, the result obtained by extracting a specific voice (for example, a specific phrase uttered by the user) from the sound detected by the microphone is used as one index indicating the mental state of the user. can be obtained as
　以上のように、本実施形態においては、センサ部１４０は各種センサを含むことができる。さらに、センサ部１４０は、正確な時刻を把握する時計機構（図示省略）を内蔵し、取得したセンシングデータ（バイオマーカ）に当該センシングデータを取得した時刻を紐づけてもよい。また、各種センサは、先に説明したように、ウェアラブルデバイス１００のセンサ部１４０内に設けられていなくてもよく、例えば、ウェアラブルデバイス１００とは別体のものとして設けられていてもよく、ユーザが使用するデバイス等に設けられていてもよい。 As described above, in this embodiment, the sensor unit 140 can include various sensors. Furthermore, the sensor unit 140 may incorporate a clock mechanism (not shown) that grasps the correct time, and associate the time when the sensing data (biomarker) is acquired with the acquired sensing data. Further, as described above, the various sensors may not be provided in the sensor unit 140 of the wearable device 100. For example, they may be provided separately from the wearable device 100. may be provided in a device or the like used by
　（通信部１５０）
　通信部１５０は、ウェアラブルデバイス１００内に設けられ、ユーザ端末２００等の外部装置との間で情報の送受信を行うことができる。言い換えると、通信部１５０は、データの送受信を行う機能を有する通信インタフェースと言える。なお、通信部１５０は、通信アンテナ、送受信回路やポート等の通信デバイスにより実現される。 (Communication unit 150)
The communication unit 150 is provided within the wearable device 100 and can transmit and receive information to and from an external device such as the user terminal 200 . In other words, the communication unit 150 can be said to be a communication interface having a function of transmitting and receiving data. Note that the communication unit 150 is implemented by communication devices such as a communication antenna, a transmission/reception circuit, and a port.
　（記憶部１６０）
　記憶部１６０は、ウェアラブルデバイス１００内に設けられ、上述した制御部１３０が各種処理を実行するためのプログラム、情報等や、処理によって得た情報を格納する。なお、記憶部１６０は、例えば、フラッシュメモリ（ｆｌａｓｈ　ｍｅｍｏｒｙ）等の不揮発性メモリ（ｎｏｎｖｏｌａｔｉｌｅ　ｍｅｍｏｒｙ）等により実現される。 (storage unit 160)
The storage unit 160 is provided in the wearable device 100 and stores programs, information, etc. for the above-described control unit 130 to execute various processes, and information obtained by the processes. Note that the storage unit 160 is realized by, for example, a nonvolatile memory such as a flash memory.
　先に説明したように、ウェアラブルデバイス１００としては、腕輪型、ＨＭＤ型等の各種の方式のウェアラブルデバイスを採用することができる。図３に、本実施形態に係るウェアラブルデバイス１００の外観の一例を示す。図３に示すように、当該ウェアラブルデバイス１００は、ユーザの手首に装着される腕輪型のウェアラブルデバイスである。 As described above, as the wearable device 100, various types of wearable devices such as bracelet type and HMD type can be adopted. FIG. 3 shows an example of the appearance of the wearable device 100 according to this embodiment. As shown in FIG. 3, the wearable device 100 is a bracelet-type wearable device worn on the user's wrist.
　詳細には、図３に示すように、ウェアラブルデバイス１００は、ベルト状のバンド部１７０と、制御ユニット１８０とを有する。バンド部１７０は、例えば、ユーザの手首に巻きつけるように装着されることから、手首の形状に合わせてリング状の形態になるように、柔らかいシリコンゲル等の材料で形成されている。また、制御ユニット１８０は、上述の制御部１３０、センサ部１４０等が設けられる部分である。さらに、センサ部１４０は、ウェアラブルデバイス１００がユーザの身体の一部に装着された際に、当該ユーザの身体に接する、又は、対向するような位置に設けられている。 Specifically, as shown in FIG. 3 , the wearable device 100 has a belt-shaped band portion 170 and a control unit 180 . For example, since the band part 170 is worn around the user's wrist, it is made of a soft material such as silicone gel so as to have a ring-like shape that matches the shape of the wrist. Further, the control unit 180 is a portion where the above-described control section 130, sensor section 140, and the like are provided. Furthermore, the sensor unit 140 is provided at a position such that when the wearable device 100 is worn on a part of the user's body, it contacts or faces the user's body.
　なお、本実施形態に係るウェアラブルデバイス１００は、図２に示される構成例や図３に示される外観に限定されるものではなく、例えば、後述するユーザ端末２００と一体のものであってもよい。 The wearable device 100 according to the present embodiment is not limited to the configuration example shown in FIG. 2 or the appearance shown in FIG. .
　＜２．３　ユーザ端末＞
　次に、図４を参照して、本実施形態に係るユーザ端末２００の構成の一例について、説明する。なお、医療機関端末４００についても同様の説明が可能である。図４は、本実施形態に係るユーザ端末２００の機能構成の一例を示すブロック図である。本実施形態に係るユーザ端末２００は、図４に示すように、入力部２１０と、出力部２２０と、制御部２３０と、通信部２５０と、記憶部２６０とを主に有する。以下に、ユーザ端末２００の有する各機能部について説明する。 <2.3 User terminal>
Next, an example of the configuration of the user terminal 200 according to this embodiment will be described with reference to FIG. The same explanation can be applied to the medical institution terminal 400 as well. FIG. 4 is a block diagram showing an example of the functional configuration of the user terminal 200 according to this embodiment. The user terminal 200 according to this embodiment mainly includes an input unit 210, an output unit 220, a control unit 230, a communication unit 250, and a storage unit 260, as shown in FIG. Each functional unit of the user terminal 200 will be described below.
　（入力部２１０）
　入力部２１０は、ユーザ端末２００へのデータ、コマンドの入力を受け付けることができる。より具体的には、当該入力部２１０は、タッチパネル、キーボード、マイク等により実現される。 (Input unit 210)
The input unit 210 can accept input of data and commands to the user terminal 200 . More specifically, the input unit 210 is implemented by a touch panel, keyboard, microphone, or the like.
　（出力部２２０）
　出力部２２０は、例えば、ディスプレイ、スピーカ、ランプ、映像出力端子、音声出力端子等により構成され、画像、点滅、音声等により各種の情報をユーザへ出力することができる。 (Output unit 220)
The output unit 220 is composed of, for example, a display, a speaker, a lamp, a video output terminal, an audio output terminal, etc., and can output various information to the user by means of images, flashes, sounds, and the like.
　（制御部２３０）
　制御部２３０は、ユーザ端末２００内に設けられ、ユーザ端末２００の各機能部を制御したり、ウェアラブルデバイス１００からのバイオマーカ（センシングデータ）を取得したりすることができる。具体的には、制御部２３０は、例えば、ＣＰＵ（Ｃｅｎｔｒａｌ　Ｐｒｏｃｅｓｓｉｎｇ　Ｕｎｉｔ）、ＲＯＭ（Ｒｅａｄ　Ｏｎｌｙ　Ｍｅｍｏｒｙ）、ＲＡＭ（Ｒａｎｄｏｍ　Ａｃｃｅｓｓ　Ｍｅｍｏｒｙ）等により実現される。また、制御部２３０は、ウェアラブルデバイス１００からのセンシングデータに対して解析処理を行ってもよい。 (control unit 230)
The control unit 230 is provided in the user terminal 200 and can control each functional unit of the user terminal 200 and acquire biomarkers (sensing data) from the wearable device 100 . Specifically, the control unit 230 is realized by, for example, a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), and the like. Also, the control unit 230 may perform analysis processing on sensing data from the wearable device 100 .
　（通信部２５０）
　通信部２５０は、ウェアラブルデバイス１００やサーバ３００等の外部装置との間で情報の送受信を行うことができる。言い換えると、通信部２５０は、データの送受信を行う機能を有する通信インタフェースと言える。具体的には、通信部２５０は、通信アンテナ、送受信回路やポート等の通信デバイスにより実現される。 (Communication unit 250)
The communication unit 250 can transmit and receive information to and from external devices such as the wearable device 100 and the server 300 . In other words, the communication unit 250 can be said to be a communication interface having a function of transmitting and receiving data. Specifically, the communication unit 250 is implemented by communication devices such as a communication antenna, a transmission/reception circuit, and a port.
　（記憶部２６０）
　記憶部２６０は、上述した制御部２３０が各種処理を実行するためのプログラム、情報等や、処理によって得た情報を格納することができる。記憶部２６０は、例えば、ハードディスク（Ｈａｒｄ　Ｄｉｓｋ：ＨＤ）などの磁気記録媒体や、フラッシュメモリ（ｆｌａｓｈ　ｍｅｍｏｒｙ）などの不揮発性メモリ（ｎｏｎｖｏｌａｔｉｌｅ　ｍｅｍｏｒｙ）等により実現される。 (storage unit 260)
The storage unit 260 can store programs, information, etc. for the above-described control unit 230 to execute various processes, and information obtained by the processes. The storage unit 260 is realized by, for example, a magnetic recording medium such as a hard disk (HD), a nonvolatile memory such as a flash memory, or the like.
　なお、本実施形態に係るユーザ端末２００は、図４に示される構成例に限定されるものではなく、例えば、他の機能部をさらに含んでいてもよい。 Note that the user terminal 200 according to the present embodiment is not limited to the configuration example shown in FIG. 4, and may further include other functional units, for example.
　＜２．４　サーバ＞
　次に、図５を参照して、本実施形態に係るサーバ３００の構成について説明する。図５は、本実施形態に係るサーバ３００の機能構成の一例を示すブロック図である。詳細には、サーバ３００は、図５に示すように、入力部３１０と、出力部３２０と、処理部３３０と、通信部３５０と、記憶部３６０とを主に有する。以下に、サーバ３００の有する各機能部について説明する。 <2.4 Server>
Next, the configuration of the server 300 according to this embodiment will be described with reference to FIG. FIG. 5 is a block diagram showing an example of the functional configuration of the server 300 according to this embodiment. Specifically, the server 300 mainly includes an input unit 310, an output unit 320, a processing unit 330, a communication unit 350, and a storage unit 360, as shown in FIG. Each functional unit of the server 300 will be described below.
　（入力部３１０）
　入力部３１０は、サーバ３００へのデータ、コマンドの入力を受け付けることができる。より具体的には、当該入力部３１０は、タッチパネル、キーボード等により実現される。 (Input unit 310)
The input unit 310 can accept input of data and commands to the server 300 . More specifically, the input unit 310 is implemented by a touch panel, keyboard, or the like.
　（出力部３２０）
　出力部３２０は、例えば、ディスプレイ等により構成され、画像等により各種の情報を出力することができる。 (Output unit 320)
The output unit 320 is configured by, for example, a display or the like, and can output various information in the form of images or the like.
　（処理部３３０）
　処理部３３０は、サーバ３００の各機能部を制御することができる。当該処理部３３０は、例えば、ＣＰＵ、ＲＯＭ、ＲＡＭ等のハードウェアにより実現される。また、処理部３３０は、ウェアラブルデバイス１００からのバイオマーカ等に基づいて、ユーザに対して推薦する医療機関を決定することができる。加えて、処理部３３０は、重要度が高い項目を抽出することもできる。詳細には、処理部３３０は、上述したこれら機能を実現するために、取得部３３２、重要度算出部３３４、推薦度算出部（推薦部）３３６、及び出力制御部３３８として機能する。また、処理部３３０は、ウェアラブルデバイス１００からのセンシングデータに対して解析処理を行ってもよい。以下に、本実施形態に係る処理部３３０のこれら機能の詳細について説明する。 (Processing unit 330)
The processing unit 330 can control each functional unit of the server 300 . The processing unit 330 is realized by hardware such as CPU, ROM, and RAM, for example. Also, the processing unit 330 can determine a medical institution to recommend to the user based on the biomarkers and the like from the wearable device 100 . In addition, the processing unit 330 can also extract items with high importance. Specifically, the processing unit 330 functions as an acquisition unit 332, an importance calculation unit 334, a recommendation calculation unit (recommendation unit) 336, and an output control unit 338 in order to realize these functions described above. The processing unit 330 may also perform analysis processing on sensing data from the wearable device 100 . Details of these functions of the processing unit 330 according to the present embodiment will be described below.
　～取得部３３２～
　取得部３３２は、ウェアラブルデバイス１００からユーザ端末２００を介して送信されたバイオマーカや、ユーザ端末２００から問診情報及び背景情報（スコア）等を取得する。また、取得部３３２は、各医療機関端末４００から複数の患者の情報（バイオマーカ、問診情報、背景情報及び治療実績等）等を取得する。さらに、取得部３３２は、取得した情報を後述する重要度算出部３３４及び推薦度算出部３３６へ出力することができる。 ~ Acquisition unit 332 ~
The acquisition unit 332 acquires biomarkers transmitted from the wearable device 100 via the user terminal 200, interview information, background information (scores), and the like from the user terminal 200. FIG. Also, the acquiring unit 332 acquires information (biomarkers, interview information, background information, treatment results, etc.) of a plurality of patients from each medical institution terminal 400 . Furthermore, the acquisition unit 332 can output the acquired information to the importance calculation unit 334 and the recommendation calculation unit 336, which will be described later.
　～重要度算出部３３４～
　重要度算出部３３４は、複数の医療機関から得られた複数の患者の情報（バイオマーカ、問診情報、背景情報及び治療実績等）に対して多変量解析等の手法を用いて、情報ごとの、寛解(詳細には、寛解したか否か)に対する相関性（関連性）の高さの程度を示す指標である重要度を算出することができる。さらに、重要度算出部３３４は、算出した重要度に基づき、寛解と相関性の高い情報項目（種類）を探索することができる。また、重要度算出部３３４は、各情報（項目）の重要度等や、重要度に従って並べた情報（項目）のランキング等の情報を、後述する出力制御部３３８や記憶部３６０に出力することができる。これら重要度算出部３３４によって出力された情報は、ユーザに対して情報入力を要求する際に使用したり、後述する推薦度算出部３３６で抽出する患者の人数を決定する際に使用することができる。なお、重要度算出部３３４の動作の詳細については、後述する。 -importance calculation unit 334-
The importance calculation unit 334 uses a technique such as multivariate analysis for a plurality of patient information (biomarkers, medical interview information, background information, treatment results, etc.) obtained from a plurality of medical institutions, for each information , the degree of importance, which is an index indicating the degree of high correlation (relevance) to remission (more specifically, whether or not remission has occurred) can be calculated. Furthermore, the importance calculation unit 334 can search for information items (types) highly correlated with remission based on the calculated importance. In addition, the importance calculation unit 334 outputs information such as the importance of each information (item) and the ranking of the information (items) arranged according to the importance to the output control unit 338 and the storage unit 360, which will be described later. can be done. The information output by the importance calculation unit 334 can be used when requesting the user to input information, or used when determining the number of patients to be extracted by the recommendation calculation unit 336, which will be described later. can. Details of the operation of the importance calculation unit 334 will be described later.
　～推薦度算出部３３６～
　推薦度算出部３３６は、ウェアラブルデバイス１００からユーザ端末２００を介して送信されたバイオマーカ（センシングデータ）や、複数の医療機関から得られた治療実績等を解析して、ユーザに精神疾患の治療のために推薦する医療機関を決定することができる。そして、推薦度算出部３３６は、決定した医療機関の情報を後述する出力制御部３３８に出力することができる。例えば、推薦度算出部３３６は、複数の医療機関から得られた治療実績に含まれる複数の患者のバイオマーカ（本実施形態においては、背景情報、問診情報も含んでもよい）中から、ユーザのバイオマーカとの類似の程度を示す類似度を算出し、算出された類似度の高い順に、所定の数の患者を抽出することで、ユーザに類似する患者を抽出する。そして、推薦度算出部３３６は、抽出した複数の患者の治療実績（例えば、受診割合、寛解率等）に基づいて、各医療機関の推薦度を算出し、推薦度の高い医療機関をユーザに推薦する医療機関として決定する。なお、推薦度算出部３３６の動作の詳細については、後述する。 ~Recommendation degree calculation unit 336~
The recommendation degree calculation unit 336 analyzes biomarkers (sensing data) transmitted from the wearable device 100 via the user terminal 200, treatment results obtained from a plurality of medical institutions, etc., and provides the user with treatment for mental illness. can decide which medical institution to recommend for Then, the recommendation degree calculation unit 336 can output information on the determined medical institution to the output control unit 338 described later. For example, the recommendation degree calculation unit 336 selects a user's A degree of similarity indicating the degree of similarity to the biomarker is calculated, and a predetermined number of patients are extracted in descending order of the calculated degree of similarity, thereby extracting patients similar to the user. Then, the recommendation degree calculation unit 336 calculates the recommendation degree of each medical institution based on the treatment results of a plurality of extracted patients (for example, consultation rate, remission rate, etc.), and sends the highly recommended medical institution to the user. Decide as a recommended medical institution. Details of the operation of the recommendation degree calculation unit 336 will be described later.
　～出力制御部３３８～
　出力制御部３３８は、重要度算出部３３４や推薦度算出部３３６が出力した情報や、当該情報に基づいて選択される情報等を、後述する通信部３５０を制御して、ユーザ端末２００に送信することができる。 ~ Output control unit 338 ~
The output control unit 338 controls the communication unit 350, which will be described later, to transmit the information output by the importance calculation unit 334 and the recommendation calculation unit 336, information selected based on the information, and the like to the user terminal 200. can do.
　（通信部３５０）
　通信部３５０は、ユーザ端末２００や医療機関端末４００等の外部装置との間で情報の送受信を行うことができる。言い換えると、通信部３５０は、データの送受信を行う機能を有する通信インタフェースと言え、具体的には、通信アンテナ、送受信回路やポート等の通信デバイスにより実現される。 (Communication unit 350)
The communication unit 350 can transmit and receive information to and from external devices such as the user terminal 200 and the medical institution terminal 400 . In other words, the communication unit 350 can be said to be a communication interface having a function of transmitting and receiving data, and is specifically realized by a communication device such as a communication antenna, a transmission/reception circuit, and a port.
　（記憶部３６０）
　記憶部３６０は、上述した処理部３３０が各種処理を実行するためのプログラム、情報等や、処理によって得た情報を格納することができる。詳細には、記憶部３６０は、図５に示すように、各医療機関から取得した治療実績（例えば、患者が寛解したか否かの情報や、寛解までの治療期間、治療方法、投薬情報、担当医師等の情報）を格納する医療機関情報テーブル３６２を格納することができる。また、記憶部３６０は、上述した推薦度算出部３３６で算出した重要度に従って並べた情報のランキングを格納する重要度ランキングテーブル３６４を格納することができる。さらに、記憶部３６０は、ユーザ端末２００から得られたユーザの各種情報（例えば、バイオマーカ、問診情報、背景情報）を格納するユーザ情報テーブル３６６を格納することができる。記憶部３６０は、例えば、ハードディスク等の磁気記録媒体や、フラッシュメモリ等の不揮発性メモリ等により実現される。なお、上述した情報にはユーザや患者のプライバシーにかかわる情報が含まれることから、本実施形態においては、処理部３３０で処理を行う際にのみ、記憶部３６０に一時的上記情報を格納し、処理が終了した後には、すぐに消去することが好ましい。また、本実施形態においては、個々の患者が特定できないように、情報処理（例えば、患者の指名の代わりに、単なる文字列からなる識別情報を使用する等）が施されていることが好ましい。 (storage unit 360)
The storage unit 360 can store programs, information, and the like for the processing unit 330 described above to execute various types of processing, and information obtained by the processing. Specifically, as shown in FIG. 5, the storage unit 360 stores the treatment results obtained from each medical institution (for example, information on whether the patient is in remission, treatment period until remission, treatment method, medication information, A medical institution information table 362 can be stored that stores information such as doctors in charge. The storage unit 360 can also store an importance ranking table 364 that stores the ranking of information arranged according to the importance calculated by the recommendation calculation unit 336 described above. Furthermore, the storage unit 360 can store a user information table 366 that stores various types of user information (eg, biomarkers, interview information, background information) obtained from the user terminal 200 . The storage unit 360 is implemented by, for example, a magnetic recording medium such as a hard disk, or a non-volatile memory such as a flash memory. In addition, since the above information includes information related to the privacy of the user and the patient, in the present embodiment, the above information is temporarily stored in the storage unit 360 only when processing is performed by the processing unit 330, It is preferable to erase immediately after the processing is finished. In addition, in this embodiment, it is preferable that information processing (for example, using identification information consisting of a simple character string instead of naming a patient) is performed so that individual patients cannot be identified.
　なお、本実施形態に係るサーバ３００は、図５に示される構成例に限定されるものではなく、例えば、他の機能部をさらに含んでいてもよい。さらに、サーバ３００は、ネットワーク５００で互いに通信可能に接続された複数の情報処理装置から構成されてもよい。また、サーバ３００の機能の少なくとも一部は、上述したユーザ端末２００によって実行されてもよく、もしくは、サーバ３００は、ユーザ端末２００又は医療機関端末４００と一体となった装置として構成されてもよい。 Note that the server 300 according to the present embodiment is not limited to the configuration example shown in FIG. 5, and may further include other functional units, for example. Furthermore, the server 300 may be composed of a plurality of information processing devices communicably connected to each other via the network 500 . At least part of the functions of the server 300 may be executed by the user terminal 200 described above, or the server 300 may be configured as a device integrated with the user terminal 200 or the medical institution terminal 400. .
　＜２．５　情報処理方法＞
　以上、本開示の実施形態に係る情報処理システム１０及び当該情報処理システム１０に含まれる各装置の詳細について説明した。次に、本実施形態に係る情報処理方法について、図６から図１４を参照して説明する。図６は、本実施形態に係る情報処理方法のシーケンス図であり、図７は、本実施形態に係る医療機関情報テーブル３６２の一例を示す図である。また、図８、図１０及び図１２は、本実施形態に係る情報処理方法のフローチャートであり、図９は、本実施形態に係るユーザ端末２００の表示の一例を示す説明図である。さらに、図１１は、本実施形態に係る重要度算出部３３４での情報処理方法を説明するための説明図であり、図１３及び図１４は、本実施形態に係る推薦度算出部３３６での情報処理方法を説明するための説明図である。 <2.5 Information processing method>
The details of the information processing system 10 and each device included in the information processing system 10 according to the embodiment of the present disclosure have been described above. Next, an information processing method according to this embodiment will be described with reference to FIGS. 6 to 14. FIG. FIG. 6 is a sequence diagram of the information processing method according to this embodiment, and FIG. 7 is a diagram showing an example of the medical institution information table 362 according to this embodiment. 8, 10 and 12 are flowcharts of the information processing method according to this embodiment, and FIG. 9 is an explanatory diagram showing an example of display on the user terminal 200 according to this embodiment. Furthermore, FIG. 11 is an explanatory diagram for explaining the information processing method in the importance calculation unit 334 according to this embodiment, and FIGS. It is an explanatory view for explaining an information processing method.
　（情報処理システム全体）
　まずは、図６及び図７を参照して、本実施形態に係る情報処理システムで実施される情報処理方法を説明する。図６に示すように、本実施形態に係る情報処理方法には、ステップＳ１００からステップＳ１１００までの複数のステップが含まれている。以下に、本実施形態に係る情報処理方法に含まれる各ステップの詳細を説明する。 (Entire information processing system)
First, with reference to FIGS. 6 and 7, an information processing method performed by the information processing system according to the present embodiment will be described. As shown in FIG. 6, the information processing method according to this embodiment includes a plurality of steps from step S100 to step S1100. Details of each step included in the information processing method according to the present embodiment will be described below.
　まず、ユーザ端末２００は、ユーザについての背景情報（スコア）、問診情報及びバイオマーカデータ等を含むユーザ情報を取得し、サーバ３００へ送信する（ステップＳ１００）。なお、ステップＳ１００の詳細は後述する。 First, the user terminal 200 acquires user information including background information (score) about the user, interview information, biomarker data, etc., and transmits the acquired information to the server 300 (step S100). Details of step S100 will be described later.
　サーバ３００は、ユーザ端末２００から上記ユーザの情報を受信する（ステップＳ２００）。 The server 300 receives the user information from the user terminal 200 (step S200).
　医療機関端末４００は、医療従事者から医療機関端末を介して入力された、患者の治療実績（例えば、患者が寛解したか否かの情報や、寛解までの治療期間、治療方法、投薬情報、担当医師等の情報）や、受診開始時のバイオマーカ、背景情報（スコア）及び問診情報等を含む治療情報を取得する（ステップＳ３００）。例えば、各医療機関に設置された医療機関端末４００の記憶部（図示省略）は、図７に示すように、患者の属性情報（例えば、性別、年齢等）、医療機関に受診を開始した時点での患者のバイオマーカ、背景情報（スコア）、問診情報、患者の治療実績（例えば、患者が寛解したか否かの情報や、寛解までの治療期間、治療方法、投薬情報、担当医師等の情報）等を含む医療機関情報テーブル３６２を格納する。なお、図７においては、寛解した場合には「Ｙ」で示し、併せて寛解までにかかった治療期間も表示されている。さらに、図７においては、寛解していない場合には「Ｎ」で示されている。また、上記治療実績には、医療従事者から入力される上記情報の他に、治療に対する患者の満足度、治療終了後の回復の度合い等、患者の主観に基づく情報も含んでもよい。このような患者の主観の情報は、医療機関又は本実施形態に係る情報処理システムの運営機関等によって、患者に対する定期又は非定期に実施されるアンケートによって収集することができる。 The medical institution terminal 400 receives patient treatment results (for example, information on whether or not the patient is in remission, treatment period until remission, treatment method, medication information, information of the doctor in charge, etc.), biomarkers at the start of the medical examination, background information (score), interview information, etc. are acquired (step S300). For example, the storage unit (not shown) of the medical institution terminal 400 installed in each medical institution stores, as shown in FIG. patient's biomarkers, background information (score), medical interview information, patient's treatment results (for example, information on whether the patient is in remission, treatment period until remission, treatment method, medication information, doctor in charge, etc. information), etc., is stored. In FIG. 7, remission is indicated by "Y", and the treatment period required until remission is also displayed. Additionally, in FIG. 7, not in remission is indicated by "N". In addition to the information input by the medical staff, the treatment results may also include patient subjective information, such as the patient's degree of satisfaction with treatment and the degree of recovery after treatment. Such patient's subjective information can be collected by a medical institution, an operating agency of the information processing system according to the present embodiment, or the like, through a regular or non-periodic questionnaire to the patient.
　なお、本実施形態においては、医療機関情報テーブル３６２に格納されるバイオマーカは、図７に示されるように２種類のバイオマーカであることに限定されるものではなく、１種類以上であればよい。また、本実施形態においては、医療機関情報テーブル３６２に格納される背景情報（スコア）及び問診情報は、図７に示されるように１種類の背景情報（スコア）及び問診情報であることに限定されるものではなく、複数種の背景情報（スコア）及び問診情報であってもよい。本実施形態においては、最終的により好適な医療機関をユーザに推薦できることから、格納される情報の種類が多いことが好ましい。なお、ステップＳ３００の処理のタイミングは、特に限定されるものではなく、例えば、医療機関で治療情報が更新されるたびに実行されてもよく、所定の期間ごとに定期的に実行されてもよい。 In this embodiment, the biomarkers stored in the medical institution information table 362 are not limited to two types of biomarkers as shown in FIG. good. Further, in this embodiment, the background information (score) and medical inquiry information stored in the medical institution information table 362 are limited to one type of background information (score) and medical inquiry information as shown in FIG. It may be multiple types of background information (scores) and interview information. In the present embodiment, it is preferable that a large number of types of information be stored, since a more suitable medical institution can be finally recommended to the user. The timing of the processing in step S300 is not particularly limited, and may be executed each time the treatment information is updated at the medical institution, or may be executed periodically at predetermined intervals. .
　サーバ３００は、医療機関端末４００から、上述した医療機関情報テーブル３６２を取得する（ステップＳ４００）。先に説明したように、上述した情報には患者のプライバシーにかかわる情報が多く含まれることから、サーバ３００は、サーバ３００で処理を行う際にのみ一時的に医療機関端末４００が格納する医療機関情報テーブル３６２のコピーデータを取得し、処理が終了した後には、コピーデータを消去することが好ましい。なお、サーバ３００は、医療機関端末４００を介さずに、直接的に、上記医療機関情報テーブル３６２又はこれに含まれる各種情報を直接的に取得してもよい（詳細には、サーバ３００に各種情報が直接入力される）。 The server 300 acquires the medical institution information table 362 described above from the medical institution terminal 400 (step S400). As described above, the above-described information includes a lot of information related to patient privacy. It is preferable to acquire the copy data of the information table 362 and delete the copy data after the processing is completed. The server 300 may directly acquire the medical institution information table 362 or various information contained therein without going through the medical institution terminal 400 (more specifically, the server 300 may information is entered directly).
　サーバ３００は、上述のステップＳ４００において複数の医療機関から得られた治療情報に対して多変量解析等の手法を用いて、各情報の種類（項目）について、寛解(詳細には、寛解したか否か)に対する相関性（関連性）の高さの程度を示す指標である重要度を算出する（ステップＳ５００）。さらに、サーバ３００は、重要度に従って並べた情報（項目）のランキングである重要度ランキングテーブル３６４を生成する。なお、当該ステップＳ５００の処理のタイミングは、特に限定されるものではないが、例えば、医療機関で治療情報が更新されるたびに実行されてもよく、所定の期間ごとに定期的に実行されてもよい。また、ステップＳ５００の詳細は後述する。 The server 300 uses techniques such as multivariate analysis on the treatment information obtained from a plurality of medical institutions in step S400 described above to determine remission (specifically, remission or not) for each type (item) of information. or not) is calculated (step S500). Furthermore, the server 300 generates an importance ranking table 364 that ranks information (items) arranged according to importance. The timing of the processing in step S500 is not particularly limited, but may be performed each time treatment information is updated at a medical institution, or may be performed periodically at predetermined intervals. good too. Details of step S500 will be described later.
　サーバ３００は、上述のステップＳ５００において生成した重要度ランキングテーブル３６４の上位Ｂ％に含まれる情報の種類（項目）、すなわち、重要度が高い情報が、上述のステップＳ２００においてユーザ端末２００から受信した情報に含まれているか（入手出来ているか）を判定する（ステップＳ６００）。サーバ３００は、入手出来ていると判定した場合（ステップＳ６００：Ｙｅｓ）には、ステップＳ１０００の処理へ進む。一方、サーバ３００は、入手出来ていないと判定した場合（ステップＳ６００：Ｎｏ）には、ステップＳ６００の処理へ進む。 Server 300 receives the types (items) of information included in the top B% of importance ranking table 364 generated in step S500 described above, that is, information with high importance, from user terminal 200 in step S200 described above. It is determined whether it is included in the information (whether it is available) (step S600). If server 300 determines that it is available (step S600: Yes), the process proceeds to step S1000. On the other hand, when the server 300 determines that it is not available (step S600: No), it proceeds to the process of step S600.
　サーバ３００は、重要度が高い情報（項目）の入力を要求する指示をユーザ端末２００へ送信する（ステップＳ７００）。具体的には、サーバ３００は、重要度ランキングテーブル３６４の上位Ｂ％に含まれる情報のうち、ステップＳ２００においてユーザ端末２００から受信できていない情報を要求する。本実施形態においては、このような要求を行うことにより、サーバ３００は、重要度の高い情報を取得することができることから、取得した情報に基づいてユーザにより好適な医療機関を推薦することができる。なお、本実施形態においては、重要度の高い情報を、重要度ランキングテーブル３６４のうちの上位のどの範囲とするかについては、運用者が適宜設定、変更することができるものとする。 The server 300 transmits to the user terminal 200 an instruction requesting the input of information (items) of high importance (step S700). Specifically, the server 300 requests information that has not been received from the user terminal 200 in step S200 among the information included in the top B% of the importance ranking table 364 . In this embodiment, by making such a request, the server 300 can acquire information with a high degree of importance, and therefore can recommend a more suitable medical institution to the user based on the acquired information. . In the present embodiment, it is assumed that the operator can appropriately set and change the upper range of information in the importance ranking table 364 in which information with a high degree of importance is to be placed.
　ユーザ端末２００は、サーバ３００からの要求を受信する（ステップＳ８００）。さらに、ユーザ端末２００は、上記要求に基づき、強調表示やアラーム音の出力等を利用して、ユーザに該当する情報の入力を促す。そして、ユーザ端末２００は、ユーザから入力された追加情報を取得し、サーバ３００へ送信する（ステップＳ９００）。 The user terminal 200 receives the request from the server 300 (step S800). Further, based on the request, the user terminal 200 prompts the user to input the relevant information by using highlighted display, output of an alarm sound, or the like. Then, the user terminal 200 acquires the additional information input by the user and transmits it to the server 300 (step S900).
　サーバ３００は、これまでの実行したステップにおいてユーザ端末２００から取得した各種の情報に基づいて、ユーザに推薦する医療機関を決定し、決定した情報をユーザ端末２００へ送信する（ステップＳ１０００）。なお、ステップＳ１０００の詳細は後述する。そして、ユーザ端末２００は、サーバ３００から受信した、ユーザに推薦する医療機関の情報（推薦情報）をユーザに向けて表示する（ステップＳ１１００）。 The server 300 determines a medical institution to recommend to the user based on various information acquired from the user terminal 200 in the steps executed so far, and transmits the determined information to the user terminal 200 (step S1000). Details of step S1000 will be described later. Then, the user terminal 200 displays the information (recommendation information) of the medical institution recommended to the user, received from the server 300, to the user (step S1100).
　なお、本実施形態に係る情報処理方法においては、サーバ３００は、上記ステップＳ１１００から所定の期間経過後に、ユーザ端末２００を介して、ユーザに対して、実際に受信した医療機関名や、そこでの治療に対する満足度、治療終了後の回復の度合い等を問うアンケートを実施してもよい。この際、得られた情報は、治療実績情報としてサーバ３００に集積されることとなる。 In the information processing method according to the present embodiment, the server 300 sends the name of the actually received medical institution to the user via the user terminal 200 after a predetermined period of time has passed from step S1100, A questionnaire may be conducted asking about the degree of satisfaction with the treatment, the degree of recovery after the treatment is completed, and the like. At this time, the information obtained is accumulated in the server 300 as treatment performance information.
　次に、上述したステップの詳細について順次説明する。 Next, the details of the above steps will be explained in order.
　（本実施形態に係るユーザ端末２００で実施される情報処理方法）
　まずは、図８及び図９を参照して、本実施形態に係るユーザ端末２００で実施される情報処理方法（詳細には、図６のステップＳ１００）を説明する。図８に示すように、本実施形態に係る情報処理方法には、ステップＳ１０１からステップＳ１０４までの複数のステップが含まれている。以下に、本実施形態に係る情報処理方法に含まれる各ステップの詳細を説明する。 (Information processing method performed by user terminal 200 according to the present embodiment)
First, with reference to FIGS. 8 and 9, the information processing method (in detail, step S100 in FIG. 6) performed by the user terminal 200 according to the present embodiment will be described. As shown in FIG. 8, the information processing method according to this embodiment includes a plurality of steps from step S101 to step S104. Details of each step included in the information processing method according to the present embodiment will be described below.
　ユーザ端末２００は、問診内容を提示し、ユーザに入力を促す（ステップＳ１０１）。さらに、ユーザ端末２００は、上述のステップＳ１０１と同様に、図９に示すような精神状態に関する患者の背景情報の質問を提示し、ユーザに入力を促す（ステップＳ１０２）。例えば、ユーザは、質問６００と同時に表示されるボタン６０２を押す操作をすることで、質問６００に回答することができる。 The user terminal 200 presents the contents of the inquiry and prompts the user to input (step S101). Further, the user terminal 200 presents a question about the patient's background information regarding the mental state as shown in FIG. 9 and prompts the user to input, as in step S101 described above (step S102). For example, the user can answer the question 600 by pressing a button 602 displayed simultaneously with the question 600 .
　ユーザ端末２００は、ウェアラブルデバイス１００からバイオマーカデータをアップロードする（ステップＳ１０３）。そして、ユーザ端末２００は、上述したステップＳ１０１からステップＳ１０３で取得した情報をサーバ３００へ送信する（ステップＳ１０４）。 The user terminal 200 uploads biomarker data from the wearable device 100 (step S103). Then, the user terminal 200 transmits the information acquired in steps S101 to S103 described above to the server 300 (step S104).
　（本実施形態に係るサーバ３００の重要度算出部３３４で実施される情報処理方法）
　次に、図１０及び図１１を参照して、本実施形態に係る重要度算出部３３４で実施される情報処理方法（詳細には、図６のステップＳ５００）を説明する。図１０に示すように、本実施形態に係る情報処理方法には、ステップＳ５０１からステップＳ５０４までの複数のステップが含まれている。以下に、本実施形態に係る情報処理方法に含まれる各ステップの詳細を説明する。 (Information processing method performed by the importance calculation unit 334 of the server 300 according to the present embodiment)
Next, the information processing method (in detail, step S500 in FIG. 6) performed by the importance calculation unit 334 according to the present embodiment will be described with reference to FIGS. 10 and 11. FIG. As shown in FIG. 10, the information processing method according to this embodiment includes a plurality of steps from step S501 to step S504. Details of each step included in the information processing method according to the present embodiment will be described below.
　本実施形態においては、ユーザの問診情報及び背景情報のそれぞれについて、複数の情報を取得することを前提としており、なるべく多くの情報を用いて当該ユーザに推薦する医療機関を決定することにより、より好適な医療機関を推薦することができる。しかしながら、これら情報のうち、精神疾患の寛解に強く相関性のあるものと、ないものとが存在する。そこで、本実施形態においては、少なくとも寛解に強く相関性の持つ（重要度の高い）情報を予め探索し、相関性の高い情報については洩れがないようにユーザに入力を要求する。このようにすることで、サーバ３００は、寛解に強く相関性の持つ（重要度の高い）情報をより確実にユーザから取得できるようになることから、ユーザにより好適な医療機関を推薦することができる。 In the present embodiment, it is assumed that a plurality of pieces of information are acquired for each of the medical interview information and the background information of the user. A suitable medical institution can be recommended. However, some of this information is strongly correlated with remission of psychiatric disorders, and some is not. Therefore, in the present embodiment, at least information strongly correlated with remission (highly important) is searched in advance, and the user is requested to input the highly correlated information without omission. By doing so, the server 300 can more reliably acquire information that is strongly correlated (highly important) with remission from the user, and thus can recommend a more suitable medical institution to the user. can.
　まず、サーバ３００は、全ての医療機関の治療情報（患者の治療実績（例えば、患者が寛解したか否かの情報や、寛解までの治療期間、治療方法、投薬情報、担当医師等の情報）や、受診開始時のバイオマーカ、背景情報（スコア）及び問診情報）のデータを取得する（ステップＳ５０１）。当該ステップＳ５０１においては、取得する情報の種類は限定されるものではないが、算出される重要度の精度が高くなることから、なるべく多くの種類の情報を用いることが好ましい。 First, the server 300 collects treatment information of all medical institutions (patient treatment results (for example, information on whether or not the patient is in remission, treatment period until remission, treatment method, medication information, information on the doctor in charge, etc.) , biomarkers, background information (score), and interview information at the start of medical examination) are acquired (step S501). In step S501, the type of information to be acquired is not limited, but it is preferable to use as many types of information as possible because the accuracy of the calculated degree of importance increases.
　サーバ３００は、ステップＳ５０１で取得した治療情報のうち、患者の精神疾患が寛解したか否かの情報を推論結果として設定する（ステップＳ５０２）。 The server 300 sets, as an inference result, information on whether or not the patient's mental illness is in remission, among the treatment information acquired in step S501 (step S502).
　サーバ３００は、寛解したか否かの情報以外の各項目の情報を変数とし、機械学習の手法を用いて、各項目の重要度を算出する（ステップＳ５０３）。例えば、重要度の算出方法としては、例えば、ランダムフォレストや、ＬｉｇｈｔＧＢＭ等の手法を挙げることができる。なお、本実施形態においては、ランダムフォレスト等に限定されるものではなく、教師あり学習を行い、変数の重要度が算出することができる手法であれば、特に限定されるものではない。 The server 300 uses the information on each item other than the information on whether or not the patient is in remission as a variable, and uses a machine learning technique to calculate the importance of each item (step S503). For example, methods such as random forest, LightGBM, and the like can be used as methods for calculating the degree of importance. Note that the present embodiment is not limited to a random forest or the like, and is not particularly limited as long as it is a method that can perform supervised learning and calculate the importance of variables.
　本実施形態においては、例えば、サーバ３００の重要度算出部３３４は、サポートベクターレグレッションやディープニューラルネットワーク等の教師付き学習器であるものとする。そして、例えば、図１１に示すように、重要度算出部３３４に、患者ａの各種バイオマーカ１－ａ、２－ａ、スコア（背景情報）１－ａ、２－ａ等からなる情報群と、患者ａが寛解したか否かの情報（寛解ａ)とを入力する。さらに、同様に、複数の患者の情報群（バイオマーカ１－ｎ、２－ｎ、スコア１－ｎ、２－ｎ等）と各患者が寛解したか否かの情報（寛解ｎ)を入力する。そして、重要度算出部３３４は、所定の規則に従って、情報群の各情報と寛解情報との間の関係について機械学習を行い、寛解との相関性の高さ、すなわち、重要度を算出する。 In this embodiment, for example, the importance calculation unit 334 of the server 300 is assumed to be a supervised learning device such as support vector regression or deep neural network. Then, for example, as shown in FIG. 11, an information group consisting of various biomarkers 1-a, 2-a, scores (background information) 1-a, 2-a, etc. of the patient a is stored in the importance calculation unit 334. , and information on whether or not patient a is in remission (remission a). Furthermore, in the same way, input information groups of multiple patients (biomarkers 1-n, 2-n, scores 1-n, 2-n, etc.) and information on whether each patient is in remission (remission n) . Then, the importance calculation unit 334 performs machine learning on the relationship between each piece of information in the information group and remission information according to a predetermined rule, and calculates the degree of correlation with remission, that is, the importance.
　そして、サーバ３００は、上述したステップＳ５０３で算出した重要度に基づき、各情報（項目）をその重要度に従って並べた重要度ランキングテーブル３６４を生成する（ステップＳ５０４）。 Then, based on the importance calculated in step S503, the server 300 generates an importance ranking table 364 in which each piece of information (item) is arranged according to its importance (step S504).
　以上のように、本実施形態においては、寛解に強く相関性の持つ（重要度の高い）情報を予め探索し、相関性の高い情報については洩れがないようにユーザに入力を要求することで、重要度の高い情報をより確実にユーザから取得できるようになることから、ユーザにより好適な医療機関を推薦することができる。 As described above, in the present embodiment, by searching in advance for information that is highly correlated (highly important) in remission, and requesting the user to input highly correlated information without omission. , since it becomes possible to obtain information of high importance from the user more reliably, it is possible to recommend a more suitable medical institution to the user.
　（本実施形態に係るサーバ３００の推薦度算出部３３６で実施される情報処理方法）
　次に、図１２から図１４を参照して、本実施形態に係る推薦度算出部３３６で実施される情報処理方法（詳細には、図６のステップＳ１０００）を説明する。図１２に示すように、本実施形態に係る情報処理方法には、ステップＳ９０１からステップＳ９０６までの複数のステップが含まれている。以下に、本実施形態に係る情報処理方法に含まれる各ステップの詳細を説明する。 (Information processing method performed by the recommendation level calculation unit 336 of the server 300 according to the present embodiment)
Next, an information processing method (more specifically, step S1000 in FIG. 6) performed by the recommendation degree calculation unit 336 according to the present embodiment will be described with reference to FIGS. 12 to 14. FIG. As shown in FIG. 12, the information processing method according to this embodiment includes a plurality of steps from step S901 to step S906. Details of each step included in the information processing method according to the present embodiment will be described below.
　事前に、サーバ３００は、対象となる医療機関を決定する。例えば、全ての医療機関であってもよく、ユーザが通院可能な範囲に所在する医療機関に限定してもよく、もしくは、ユーザから事前に指定された条件（例えば、規模、所属医者の数、所属医療従事者の数、施設内容等）によって対象とする医療機関を限定してもよい。 The server 300 determines the target medical institution in advance. For example, it may be all medical institutions, it may be limited to medical institutions located within a range that the user can visit, or conditions specified in advance by the user (for example, size, number of affiliated doctors, The target medical institutions may be limited according to the number of affiliated medical professionals, facilities, etc.).
　まず、サーバ３００は、対象となる医療機関から１つの医療機関を選択する（ステップＳ９０１）。 First, the server 300 selects one medical institution from the target medical institutions (step S901).
　サーバ３００は、ユーザの問診情報又は背景情報（スコア）に基づき、対象医療機関のデータテーブル内にある患者について、問診情報又は背景情報に関してユーザとの類似度を算出し、類似度が高いＭ人の患者を抽出し、その寛解率を算出する（ステップＳ９０２）。詳細には、サーバ３００は、例えば、上述したステップＳ９０１で選択した医療機関の治療情報に含まれる患者の問診情報を用いて、各患者が、ユーザの問診情報とどの程度類似するかを示す類似度を算出する。さらに、サーバ３００は、類似度が高い順にＭ人を選択し、選択した患者の寛解率を算出する。なお、上記Ｍ人は、任意に設定することができ、例えば、重要度算出部３３４により算出された重要度に基づいて決定してもよく、予めユーザにより設定されてもよい。また、本実施形態においては、類似度の算出は、例えば、ユーグリッド距離の差、コサイン類似度等を用いて算出することができるが、特に限定されるものではない。例えば、図１３に示すように、医療機関Ａについては、上記Ｍを１００とした場合、そのうち、寛解した人数を１０人とすると、寛解率は１０％となる。 Based on the interview information or background information (score) of the user, the server 300 calculates the degree of similarity with the user regarding the interview information or background information for the patients in the data table of the target medical institution, and patients are extracted and their remission rate is calculated (step S902). Specifically, the server 300 uses patient interview information included in the treatment information of the medical institution selected in step S901 described above, for example, to generate a similarity index indicating how similar each patient is to the user's interview information. Calculate degrees. Furthermore, the server 300 selects M patients in descending order of similarity, and calculates the remission rate of the selected patients. Note that the above M persons can be arbitrarily set, for example, may be determined based on the degree of importance calculated by the importance degree calculation unit 334, or may be set in advance by the user. In addition, in the present embodiment, similarity can be calculated using, for example, a difference in Euclidean distance, a cosine similarity, or the like, but is not particularly limited. For example, as shown in FIG. 13, assuming that M is 100, the remission rate is 10% if 10 of them are in remission, as shown in FIG.
　なお、本実施形態においては、問診情報及び背景情報の両方のそれぞれについて、ステップＳ９０２の処理を行ってもよく、もしくは、ステップＳ９０２の処理を省略してもよい。 It should be noted that in the present embodiment, the process of step S902 may be performed for both the medical interview information and the background information, or the process of step S902 may be omitted.
　サーバ３００は、ユーザのバイオマーカに基づき、対象医療機関のデータテーブル内にある患者について、バイオマーカに関してユーザとの類似度を算出し、類似度が高いＬ人の患者を抽出し、寛解率を算出する（ステップＳ９０３）。詳細には、サーバ３００は、上述したステップＳ９０１で選択した医療機関の治療データに含まれる患者のバイオマーカを用いて、各患者が、ユーザのバイオマーカとどの程度類似するかを示す類似度を算出する。さらに、サーバ３００は、類似度が高い順にＬ人を選択し、選択した患者の寛解率を算出する。なお、上記Ｌ人は、任意に設定することができ、例えば、重要度算出部３３４により算出された重要度に基づいて決定してもよく、予めユーザにより設定されてもよい。また、ここでも、類似度の算出は、例えば、ユーグリッド距離の差、コサイン類似度等を用いて算出することができるが、特に限定されるものではない。例えば、図１３に示すように、医療機関Ａについては、上記Ｌを１００とした場合、そのうち、寛解した人数を９０人とすると、寛解率は９０％となる。 Based on the user's biomarker, the server 300 calculates the similarity with the user in terms of biomarkers for patients in the data table of the target medical institution, extracts L patients with high similarity, and calculates the remission rate. Calculate (step S903). Specifically, the server 300 uses the patient's biomarkers included in the treatment data of the medical institution selected in step S901 described above to calculate the degree of similarity that indicates how similar each patient is to the user's biomarkers. calculate. Furthermore, the server 300 selects L patients in descending order of similarity and calculates the remission rate of the selected patients. Note that the L persons can be arbitrarily set, and for example, may be determined based on the importance calculated by the importance calculation unit 334, or may be set in advance by the user. Also here, the similarity can be calculated using, for example, a difference in Eugrid distance, a cosine similarity, or the like, but is not particularly limited. For example, as shown in FIG. 13, assuming that L is 100 and 90 of them are in remission, the remission rate is 90%.
　サーバ３００は、上述したステップＳ９０１で選択した医療機関の推薦度を算出する（ステップＳ９０４）。例えば、図１３に示すように、３つの項目（問診情報、背景情報、及び、バイオマーカ）の寛解度を算出した場合には、その平均値を推薦度としてもよい。具体的には、図１３に示す医療機関Ａでは、平均値が４０％となるため、推薦度を４０とする。なお、本実施形態においては、推薦度の算出は、平均値に限定されるものではなく、各項目に重みづけした上で平均値を算出してもよい。 The server 300 calculates the recommendation level of the medical institution selected in step S901 (step S904). For example, as shown in FIG. 13, when the degree of remission of three items (interview information, background information, and biomarker) is calculated, the average value thereof may be used as the degree of recommendation. Specifically, in the medical institution A shown in FIG. 13, the average value is 40%, so the recommendation level is set to 40. Note that in the present embodiment, the calculation of the recommendation level is not limited to the average value, and the average value may be calculated after weighting each item.
　サーバ３００は、全ての対象となる医療機関の推薦度を算出したかを判定する（ステップＳ９０５）。サーバ３００は、全ての対象となる医療機関の推薦度を算出したと判定した場合（ステップＳ９０５：Ｙｅｓ）には、ステップＳ９０６の処理へ進む。一方、サーバ３００は、全ての対象となる医療機関の推薦度を算出していないと判定した場合（ステップＳ９０５：Ｎｏ）には、ステップＳ９０１の処理へ戻る。本実施形態においては、このようにして、全ての対象となる医療機関の推薦度を算出する。 The server 300 determines whether the degrees of recommendation for all target medical institutions have been calculated (step S905). If the server 300 determines that the recommendation levels of all target medical institutions have been calculated (step S905: Yes), the process proceeds to step S906. On the other hand, when the server 300 determines that the recommendation levels of all target medical institutions have not been calculated (step S905: No), the process returns to step S901. In this embodiment, the recommendation degrees of all target medical institutions are calculated in this way.
　サーバ３００は、推薦度が高い医療機関をユーザに推薦する（ステップＳ９０６）。例えば、図１４に示す例では、推薦度が最も高いのは推薦度７０の医療機関Ｂであるため、サーバ３００は医療機関Ｂを推薦する。 The server 300 recommends medical institutions with a high degree of recommendation to the user (step S906). For example, in the example shown in FIG. 14, the server 300 recommends the medical institution B, which has the highest recommendation degree of 70, because the medical institution B has the highest recommendation degree.
　以上のように、本実施形態によれば、容易に、且つ、日常的に検出することが可能なバイオマーカを用いることにより、当該ユーザに精神疾患の治療に好適な医療機関を推薦することができる。 As described above, according to the present embodiment, by using biomarkers that can be easily and routinely detected, it is possible to recommend a medical institution suitable for treatment of mental illness to the user. can.
　なお、ステップＳ９０６において、複数の医療機関で推薦度が同一になった場合には、複数の医療機関を推薦してもよく、もしくは、ステップＳ９０２及びステップＳ９０３で用いられる抽出する患者の人数（Ｍ人、Ｌ人等）を違う値に再設定した上で、上述した情報処理方法を再度実施するようにしてもよい。 In step S906, if a plurality of medical institutions have the same recommendation level, a plurality of medical institutions may be recommended, or the number of patients to be extracted (M person, L person, etc.) may be reset to a different value, and then the above-described information processing method may be performed again.
　＜３．　変形例＞
　＜３．１　変形例１＞
　また、本開示の実施形態においては、推薦度算出部３３６で実施される情報処理方法については、医療機関ごとの寛解率を算出する代わりに、全医療機関における全患者とユーザとの間の類似度を算出し、ユーザと類似する患者の治療実績のある医療機関をユーザに推薦してもよい。そこで、以下に、図１５及び図１６を参照して、このような本開示の実施形態の変形例１の詳細を説明する。図１５は、本実施形態の変形例１に係る情報処理方法のフローチャートであり、図１６は、本実施形態の変形例１に係る推薦度算出部３３６での情報処理方法を説明するための説明図である。 <3. Variation>
<3.1 Modification 1>
In the embodiment of the present disclosure, the information processing method performed by the recommendation degree calculation unit 336 is based on the similarity between all patients and users at all medical institutions, instead of calculating the remission rate for each medical institution. A medical institution that has a track record of treating patients similar to the user may be recommended to the user. Therefore, the details of the modified example 1 of the embodiment of the present disclosure will be described below with reference to FIGS. 15 and 16. FIG. FIG. 15 is a flowchart of the information processing method according to Modification 1 of the present embodiment, and FIG. 16 is an explanation for explaining the information processing method in the recommendation level calculation unit 336 according to Modification 1 of the present embodiment. It is a diagram.
　詳細には、図１５に示すように、本変形例１に係る情報処理方法には、ステップＳ９１１からステップＳ９１５までの複数のステップが含まれている。以下に、本変形例１に係る情報処理方法に含まれる各ステップの詳細を説明する。 Specifically, as shown in FIG. 15, the information processing method according to Modification 1 includes a plurality of steps from step S911 to step S915. Details of each step included in the information processing method according to Modification 1 will be described below.
　まず、サーバ３００は、全ての医療機関の治療情報を取得し、ユーザの問診情報に基づき、全ての医療機関のデータテーブル内にある患者について、問診情報に関してユーザとの類似度を算出する。さらに、サーバ３００は、類似度が高いＭ人の患者を抽出し、抽出した患者の医療機関ごとの受診割合を算出する（ステップＳ９１１）。 First, the server 300 acquires treatment information from all medical institutions, and based on the user's interview information, calculates the degree of similarity with the user regarding the interview information for patients in the data tables of all medical institutions. Further, the server 300 extracts M patients with a high degree of similarity, and calculates the consultation rate of the extracted patients for each medical institution (step S911).
　サーバ３００は、全ての医療機関の治療情報を取得し、ユーザの背景情報に基づき、全ての医療機関のデータテーブル内にある患者について、背景情報に関してユーザとの類似度を算出する。さらに、サーバ３００は、類似度が高いＬ人の患者を抽出し、抽出した患者の医療機関ごとの受診割合を算出する（ステップＳ９１２）。 The server 300 acquires treatment information of all medical institutions, and based on the user's background information, calculates the degree of similarity with the user in terms of background information for patients in the data tables of all medical institutions. Furthermore, the server 300 extracts L patients with a high degree of similarity, and calculates the consultation rate of the extracted patients for each medical institution (step S912).
　サーバ３００は、全ての医療機関の治療情報を取得し、ユーザのバイオマーカに基づき、全ての医療機関のデータテーブル内にある患者について、バイオマーカに関してユーザとの類似度を算出する。さらに、サーバ３００は、類似度が高いＡ人の患者を抽出し、抽出した患者の医療機関ごとの受診割合を算出する（ステップＳ９１３）。 The server 300 acquires the treatment information of all medical institutions, and based on the user's biomarkers, calculates the degree of similarity with the user in terms of biomarkers for patients in the data tables of all medical institutions. Furthermore, the server 300 extracts A patients with a high degree of similarity, and calculates the consultation rate of the extracted patients for each medical institution (step S913).
　サーバ３００は、上述したステップＳ９１１からステップＳ９１３で算出した受診割合を用いて、問診情報、背景情報、バイオマーカごとに最も受診割合の高い医療機関を抽出する（ステップＳ９１４）。 The server 300 uses the consultation rates calculated in steps S911 to S913 described above to extract the medical institution with the highest consultation rate for each interview information, background information, and biomarker (step S914).
　サーバ３００は、上述したステップＳ９１４で抽出された医療機関のうち、受診割合が最も高い医療機関を推薦する医療機関として、ユーザに推薦する（ステップＳ９１５）。なお、例えば、図１６に示すように、問診情報の項目において受診割合が最も高い医療機関Ｂの受診割合と、バイオマーカの項目において受診割合が最も高い医療機関Ａの受診割合とが、同一となっている。本実施形態においては、このような場合には、予め設定されて優先する項目に従って、推薦する医療機関を決定してもよく、患者の抽出人数が多かった項目で受診割合が高い医療機関を推薦するようにしてもよい。例えば、図１６の場合には、サーバ３００は、問診情報を優先するように予め設定されていたため、問診情報における受診割合を比較し、最も高い受診割合（推薦度）を持つ医療機関Ｂが選択する。 The server 300 recommends to the user the medical institution with the highest consultation rate among the medical institutions extracted in step S914 described above (step S915). For example, as shown in FIG. 16, the consultation rate of medical institution B, which has the highest consultation rate in the medical interview information items, and the consultation rate of medical institution A, which has the highest consultation rate in the biomarker items, are the same. It's becoming In this embodiment, in such a case, the medical institution to be recommended may be determined according to a preset priority item. You may make it For example, in the case of FIG. 16, the server 300 is preset to give priority to medical interview information, so it compares consultation rates in the medical interview information and selects medical institution B with the highest consultation rate (recommendation level). do.
　以上のように、本変形例においては、ユーザと類似する患者の治療実績のある医療機関をユーザに好適な医療機関として推薦することができる。 As described above, in this modified example, it is possible to recommend a medical institution that has a track record of treating patients similar to the user as a suitable medical institution for the user.
　＜３．２　変形例２＞
　また、本開示の実施形態においては、推薦度算出部３３６で実施される情報処理方法については、各項目でユーザに類似する患者を抽出し、ユーザと類似する患者の治療実績のある医療機関をユーザに推薦してもよい。そこで、以下に、図１７及び図１８を参照して、このような本開示の実施形態の変形例２の詳細を説明する。図１７は、本実施形態の変形例２に係る情報処理方法のフローチャートであり、図１８は、本実施形態の変形例２に係る推薦度算出部３３６での情報処理方法を説明するための説明図である。 <3.2 Modification 2>
Further, in the embodiment of the present disclosure, regarding the information processing method performed by the recommendation degree calculation unit 336, patients similar to the user are extracted for each item, and medical institutions that have a track record of treating patients similar to the user are selected. May be recommended to users. Therefore, the details of the modified example 2 of the embodiment of the present disclosure will be described below with reference to FIGS. 17 and 18. FIG. FIG. 17 is a flowchart of the information processing method according to Modification 2 of the present embodiment, and FIG. 18 is an explanation for explaining the information processing method in the recommendation degree calculation unit 336 according to Modification 2 of the present embodiment. It is a diagram.
　詳細には、図１７に示すように、本変形例２に係る情報処理方法には、ステップＳ９２１からステップＳ９２５までの複数のステップが含まれている。以下に、本変形例２に係る情報処理方法に含まれる各ステップの詳細を説明する。 Specifically, as shown in FIG. 17, the information processing method according to Modification 2 includes a plurality of steps from step S921 to step S925. Details of each step included in the information processing method according to Modification 2 will be described below.
　まず、サーバ３００は、全ての医療機関の治療情報を取得し、ユーザの問診情報に基づき、全ての医療機関のデータテーブル内にある患者について、問診情報に関してユーザとの類似度を算出する。さらに、サーバ３００は、類似度が高いＭ人の患者を抽出し、抽出した患者の医療機関ごとの寛解率を算出する（ステップＳ９２１）。 First, the server 300 acquires treatment information from all medical institutions, and based on the user's interview information, calculates the degree of similarity with the user regarding the interview information for patients in the data tables of all medical institutions. Furthermore, the server 300 extracts M patients with a high degree of similarity, and calculates the remission rate of the extracted patients for each medical institution (step S921).
　サーバ３００は、全ての医療機関の治療情報を取得し、ユーザの背景情報に基づき、全ての医療機関のデータテーブル内にある患者について、背景情報に関してユーザとの類似度を算出する。さらに、サーバ３００は、類似度が高いＬ人の患者を抽出し、抽出した患者の医療機関ごとの寛解率を算出する（ステップＳ９２２）。 The server 300 acquires treatment information of all medical institutions, and based on the user's background information, calculates the degree of similarity with the user in terms of background information for patients in the data tables of all medical institutions. Furthermore, the server 300 extracts L patients with a high degree of similarity, and calculates the remission rate of the extracted patients for each medical institution (step S922).
　サーバ３００は、全ての医療機関の治療情報を取得し、ユーザのバイオマーカに基づき、全ての医療機関のデータテーブル内にある患者について、バイオマーカに関してユーザとの類似度を算出する。さらに、サーバ３００は、類似度が高いＡ人の患者を抽出し、抽出した患者の医療機関ごとの寛解率を算出する（ステップＳ９２３）。 The server 300 acquires the treatment information of all medical institutions, and based on the user's biomarkers, calculates the degree of similarity with the user in terms of biomarkers for patients in the data tables of all medical institutions. Furthermore, the server 300 extracts A patients with a high degree of similarity, and calculates the remission rate of the extracted patients for each medical institution (step S923).
　サーバ３００は、上述したステップＳ９２１からステップＳ９２３で算出した寛解率を用いて、各医療機関の寛解率の平均値を算出し、算出した平均値を推薦度とする（ステップＳ９２４）。例えば、図１８に示す例では、医療機関Ａの寛解率の平均値は４０％となり、医療機関Ａの寛解率の平均値は４０％となる。 The server 300 calculates the average remission rate of each medical institution using the remission rates calculated in steps S921 to S923 described above, and uses the calculated average as the recommendation level (step S924). For example, in the example shown in FIG. 18, the average remission rate of medical institution A is 40%, and the average remission rate of medical institution A is 40%.
　サーバ３００は、推薦度が高い医療機関をユーザに推薦する（ステップＳ９２５）。例えば、図１８に示す例では、推薦度が最も高いのは推薦度６０の医療機関Ｂであるため、サーバ３００は医療機関Ｂを推薦する。 The server 300 recommends a medical institution with a high degree of recommendation to the user (step S925). For example, in the example shown in FIG. 18, the server 300 recommends the medical institution B, which has the highest recommendation degree of 60.
　以上のように、本変形例においては、ユーザと類似する患者の寛解実績のある医療機関をユーザに好適な医療機関として推薦することができる。 As described above, in this modification, a medical institution that has a track record of remission for patients similar to the user can be recommended as a suitable medical institution for the user.
　なお、これまで説明した実施形態及び変形例においては、医療機関をユーザに推薦していたが、これに限定されるものではない。本開示の実施形態においては、例えば、個々の専門医を推薦してもよく、もしくは、専門医を属性情報（性別、年齢、治療経験年数、臨床数、専門等）に基づいてクラス分けし、当該クラスに含まれる専門医をユーザに推薦してもよい。 In addition, in the embodiments and modifications described so far, the medical institution is recommended to the user, but the present invention is not limited to this. In the embodiment of the present disclosure, for example, individual specialists may be recommended, or specialists are classified based on attribute information (sex, age, years of treatment experience, number of clinical trials, specialty, etc.), and the class may recommend a specialist included in to the user.
　＜＜４．　まとめ＞＞
　以上のように、本開示の実施形態及び変形例によれば、容易に、且つ、日常的に検出することが可能なバイオマーカを用いることにより、当該ユーザに精神疾患の治療に好適な医療機関を推薦することができる。 <<4. Summary>>
As described above, according to the embodiments and modifications of the present disclosure, by using biomarkers that can be easily and routinely detected, medical institutions suitable for treating mental disorders for the user can be recommended.
　＜＜５．　ハードウェア構成の例＞＞
　図１９は、ハードウェア構成の例を示すブロック図である。以下では、サーバ３００を例に挙げて説明する。ユーザ端末２００及び医療機関端末４００についても同様の説明が可能である。サーバ３００による各種処理は、ソフトウェアと、以下に説明するハードウェアの協働により実現される。 <<5. Example of hardware configuration >>
FIG. 19 is a block diagram showing an example of hardware configuration. The server 300 will be described below as an example. The same explanation can be given for the user terminal 200 and the medical institution terminal 400 as well. Various types of processing by the server 300 are implemented by cooperation of software and hardware described below.
　図１９に示されるように、サーバ３００は、ＣＰＵ（Ｃｅｎｔｒａｌ　Ｐｒｏｃｅｓｓｉｎｇ　Ｕｎｉｔ）９０１、ＲＯＭ（Ｒｅａｄ　Ｏｎｌｙ　Ｍｅｍｏｒｙ）９０２、ＲＡＭ（Ｒａｎｄｏｍ　Ａｃｃｅｓｓ　Ｍｅｍｏｒｙ）９０３及びホストバス９０４ａを有する。また、サーバ３００は、ブリッジ９０４、外部バス９０４ｂ、インタフェース９０５、入力装置９０６、出力装置９０７、ストレージ装置９０８、ドライブ９０９、接続ポート９１１、及び通信装置９１３を有する。サーバ３００は、ＣＰＵ９０１に代えて、又は、これとともに、ＤＳＰ（Ｄｉｇｉｔａｌ　Ｓｉｇｎａｌ　Ｐｒｏｃｅｓｓｏｒ）もしくは、ＡＳＩＣ（Ａｐｐｌｉｃａｔｉｏｎ　Ｓｐｅｃｉｆｉｃ　Ｉｎｔｅｇｒａｔｅｄ　Ｃｉｒｃｕｉｔ）等の処理回路を有してもよい。 As shown in FIG. 19, the server 300 has a CPU (Central Processing Unit) 901, a ROM (Read Only Memory) 902, a RAM (Random Access Memory) 903, and a host bus 904a. The server 300 also has a bridge 904 , an external bus 904 b , an interface 905 , an input device 906 , an output device 907 , a storage device 908 , a drive 909 , a connection port 911 and a communication device 913 . The server 300 may have a processing circuit such as a DSP (Digital Signal Processor) or an ASIC (Application Specific Integrated Circuit) in place of or in addition to the CPU 901 .
　ＣＰＵ９０１は、演算処理装置及び制御装置として機能し、各種プログラムに従ってサーバ３００内の動作全般を制御する。また、ＣＰＵ９０１は、マイクロプロセッサであってもよい。ＲＯＭ９０２は、ＣＰＵ９０１が使用するプログラムや演算パラメータ等を記憶する。ＲＡＭ９０３は、ＣＰＵ９０１の実行において使用するプログラムや、その実行において適宜変化するパラメータ等を一時記憶する。ＣＰＵ９０１は、例えば、サーバ３００の処理部３３０等を具現し得る。 The CPU 901 functions as an arithmetic processing device and a control device, and controls overall operations within the server 300 according to various programs. Alternatively, the CPU 901 may be a microprocessor. The ROM 902 stores programs, calculation parameters, and the like used by the CPU 901 . The RAM 903 temporarily stores programs used in the execution of the CPU 901, parameters that change as appropriate during the execution, and the like. The CPU 901 can embody the processing unit 330 of the server 300, for example.
　ＣＰＵ９０１、ＲＯＭ９０２及びＲＡＭ９０３は、ＣＰＵバス等を含むホストバス９０４ａにより相互に接続されている。ホストバス９０４ａは、ブリッジ９０４を介して、ＰＣＩ（Ｐｅｒｉｐｈｅｒａｌ　Ｃｏｍｐｏｎｅｎｔ　Ｉｎｔｅｒｃｏｎｎｅｃｔ／Ｉｎｔｅｒｆａｃｅ）バス等の外部バス９０４ｂに接続されている。なお、ホストバス９０４ａ、ブリッジ９０４及び外部バス９０４ｂは、お互いから分離した構成を必ずしも有する必要はなく、単一の構成（例えば１つのバス）において実装されてもよい。 The CPU 901, ROM 902 and RAM 903 are interconnected by a host bus 904a including a CPU bus and the like. The host bus 904a is connected via a bridge 904 to an external bus 904b such as a PCI (Peripheral Component Interconnect/Interface) bus. It should be noted that host bus 904a, bridge 904 and external bus 904b need not necessarily have separate configurations from each other and may be implemented in a single configuration (eg, one bus).
　入力装置９０６は、例えば、マウス、キーボード、タッチパネル、ボタン、マイク、スイッチ及びレバー等、実施者によって情報が入力される装置によって実現される。また、入力装置９０６は、例えば、赤外線やその他の電波を利用したリモートコントロール装置であってもよいし、サーバ３００の操作に対応した携帯電話やＰＤＡ（Ｐｅｒｓｏｎａｌ　Ｄｉｇｉｔａｌ　Ａｓｓｉｓｔａｎｔ）等の外部接続機器であってもよい。さらに、入力装置９０６は、例えば、上記の入力手段を用いて実施者により入力された情報に基づいて入力信号を生成し、ＣＰＵ９０１に出力する入力制御回路等を含んでいてもよい。実施者は、この入力装置９０６を操作することにより、サーバ３００に対して各種のデータを入力したり処理動作を指示したりすることができる。 The input device 906 is implemented by a device such as a mouse, keyboard, touch panel, button, microphone, switch, lever, etc., through which information is input by the practitioner. Also, the input device 906 may be, for example, a remote control device using infrared rays or other radio waves, or may be an external connection device such as a mobile phone or PDA (Personal Digital Assistant) compatible with the operation of the server 300. may Furthermore, the input device 906 may include, for example, an input control circuit that generates an input signal based on information input by the practitioner using the above input means and outputs the signal to the CPU 901 . By operating the input device 906, the practitioner can input various data to the server 300 and instruct processing operations.
　出力装置９０７は、取得した情報を実施者に対して視覚的又は聴覚的に通知することが可能な装置で形成される。このような装置として、ＣＲＴ（Ｃａｔｈｏｄｅ　Ｒａｙ　Ｔｕｂｅ）ディスプレイ装置、液晶ディスプレイ装置、プラズマディスプレイ装置、ＥＬ（Ｅｌｅｃｔｒｏ　Ｌｕｍｉｎｅｓｃｅｎｔ）ディスプレイ装置及びランプ等の表示装置や、スピーカ及びヘッドホン等の音響出力装置や、プリンタ装置等がある。 The output device 907 is formed by a device capable of visually or audibly notifying the practitioner of the acquired information. Such devices include display devices such as CRT (Cathode Ray Tube) display devices, liquid crystal display devices, plasma display devices, EL (Electro Luminescent) display devices and lamps, acoustic output devices such as speakers and headphones, and printer devices. etc.
　ストレージ装置９０８は、データ格納用の装置である。ストレージ装置９０８は、例えば、ＨＤＤ（Ｈａｒｄ　Ｄｉｓｋ　Ｄｒｉｖｅ）等の磁気記憶部デバイス、半導体記憶デバイス、光記憶デバイス又は光磁気記憶デバイス等により実現される。ストレージ装置９０８は、記憶媒体、記憶媒体にデータを記録する記録装置、記憶媒体からデータを読み出す読出し装置及び記憶媒体に記録されたデータを削除する削除装置等を含んでもよい。このストレージ装置９０８は、ＣＰＵ９０１が実行するプログラムや各種データ及び外部から取得した各種のデータ等を格納する。ストレージ装置９０８は、例えば、サーバ３００の記憶部３６０等を具現し得る。 The storage device 908 is a device for storing data. The storage device 908 is realized by, for example, a magnetic storage device such as a HDD (Hard Disk Drive), a semiconductor storage device, an optical storage device, a magneto-optical storage device, or the like. The storage device 908 may include a storage medium, a recording device that records data on the storage medium, a reading device that reads data from the storage medium, a deletion device that deletes data recorded on the storage medium, and the like. The storage device 908 stores programs executed by the CPU 901, various data, and various data acquired from the outside. The storage device 908 can embody the storage unit 360 of the server 300, for example.
　ドライブ９０９は、記憶媒体用リーダライタであり、サーバ３００に内蔵、あるいは、外付けされる。ドライブ９０９は、装着されている磁気ディスク、光ディスク、光磁気ディスク、又は、半導体メモリ等のリムーバブル記憶媒体に記録されている情報を読み出して、ＲＡＭ９０３に出力する。また、ドライブ９０９は、リムーバブル記憶媒体に情報を書き込むこともできる。 The drive 909 is a reader/writer for storage media, and is either built into the server 300 or externally attached. The drive 909 reads information recorded on a removable storage medium such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, and outputs the information to the RAM 903 . Drive 909 can also write information to a removable storage medium.
　接続ポート９１１は、外部機器と接続されるインタフェースであって、例えばＵＳＢ（Ｕｎｉｖｅｒｓａｌ　Ｓｅｒｉａｌ　Ｂｕｓ）等によりデータ伝送可能な外部機器との接続口である。 The connection port 911 is an interface connected to an external device, and is a connection port with an external device capable of data transmission by, for example, USB (Universal Serial Bus).
　通信装置９１３は、例えば、ネットワーク９２０に接続するための通信デバイス等で形成された通信インタフェースである。通信装置９１３は、例えば、有線若しくは無線ＬＡＮ（Ｌｏｃａｌ　Ａｒｅａ　Ｎｅｔｗｏｒｋ）、ＬＴＥ（Ｌｏｎｇ　Ｔｅｒｍ　Ｅｖｏｌｕｔｉｏｎ）、Ｂｌｕｅｔｏｏｔｈ（登録商標）又はＷＵＳＢ（Ｗｉｒｅｌｅｓｓ　ＵＳＢ）用の通信カード等である。また、通信装置９１３は、光通信用のルータ、ＡＤＳＬ（Ａｓｙｍｍｅｔｒｉｃ　Ｄｉｇｉｔａｌ　Ｓｕｂｓｃｒｉｂｅｒ　Ｌｉｎｅ）用のルータ又は各種通信用のモデム等であってもよい。この通信装置９１３は、例えば、インターネットや他の通信機器との間で、例えばＴＣＰ／ＩＰ（Ｔｒａｎｓｍｉｓｓｉｏｎ　Ｃｏｎｔｒｏｌ　Ｐｒｏｔｏｃｏｌ／Ｉｎｔｅｒｎｅｔ　Ｐｒｏｔｏｃｏｌ）等の所定のプロトコルに則して信号等を送受信することができる。通信装置９１３は、例えば、サーバ３００の通信部３５０等を具現し得る。 The communication device 913 is, for example, a communication interface formed by a communication device or the like for connecting to the network 920 . The communication device 913 is, for example, a communication card for wired or wireless LAN (Local Area Network), LTE (Long Term Evolution), Bluetooth (registered trademark), or WUSB (Wireless USB). The communication device 913 may be a router for optical communication, a router for ADSL (Asymmetric Digital Subscriber Line), a modem for various types of communication, or the like. For example, the communication device 913 can transmit and receive signals to and from the Internet and other communication devices in accordance with a predetermined protocol such as TCP/IP (Transmission Control Protocol/Internet Protocol). The communication device 913 can embody the communication unit 350 of the server 300, for example.
　なお、ネットワーク９２０は、ネットワーク９２０に接続されている装置から送信される情報の有線又は無線の伝送路である。例えば、ネットワーク９２０は、インターネット、電話回線網、衛星通信網等の公衆回線網や、Ｅｔｈｅｒｎｅｔ（登録商標）を含む各種のＬＡＮ（Ｌｏｃａｌ　Ａｒｅａ　Ｎｅｔｗｏｒｋ）、ＷＡＮ（Ｗｉｄｅ　Ａｒｅａ　Ｎｅｔｗｏｒｋ）等を含んでもよい。また、ネットワーク９２０は、ＩＰ－ＶＰＮ（Ｉｎｔｅｒｎｅｔ　Ｐｒｏｔｏｃｏｌ－Ｖｉｒｔｕａｌ　Ｐｒｉｖａｔｅ　Ｎｅｔｗｏｒｋ）等の専用回線網を含んでもよい。 Note that the network 920 is a wired or wireless transmission path for information transmitted from devices connected to the network 920 . For example, the network 920 may include a public network such as the Internet, a telephone network, a satellite communication network, various LANs (Local Area Networks) including Ethernet (registered trademark), WANs (Wide Area Networks), and the like. Network 920 may also include a dedicated line network such as IP-VPN (Internet Protocol-Virtual Private Network).
　以上、サーバ３００の機能を実現可能なハードウェア構成例を示した。上記の各構成要素は、汎用的な部材を用いて実現されていてもよいし、各構成要素の機能に特化したハードウェアにより実現されていてもよい。従って、本開示を実施する時々の技術レベルに応じて、適宜、利用するハードウェア構成を変更することが可能である。 An example of the hardware configuration capable of realizing the functions of the server 300 has been shown above. Each component described above may be implemented using general-purpose members, or may be implemented by hardware specialized for the function of each component. Therefore, it is possible to appropriately change the hardware configuration to be used according to the technical level at which the present disclosure is implemented.
　＜＜６．　補足＞＞
　なお、先に説明した本開示の実施形態は、例えば、上記で説明したような情報処理装置又は情報処理システムで実行される情報処理方法、情報処理装置を機能させるためのプログラム、及びプログラムが記録された一時的でない有形の媒体を含みうる。また、当該プログラムをインターネット等の通信回線（無線通信も含む）を介して頒布してもよい。 <<6. Supplement >>
Note that the above-described embodiments of the present disclosure include, for example, an information processing method executed by an information processing apparatus or an information processing system as described above, a program for operating the information processing apparatus, and a program in which the program is recorded. may include non-transitory tangible media that have been processed. Also, the program may be distributed via a communication line (including wireless communication) such as the Internet.
　また、上述した本開示の実施形態の情報処理方法における各ステップは、必ずしも記載された順序に沿って処理されなくてもよい。例えば、各ステップは、適宜順序が変更されて処理されてもよい。また、各ステップは、時系列的に処理される代わりに、一部並列的に又は個別的に処理されてもよい。さらに、各ステップの処理についても、必ずしも記載された方法に沿って処理されなくてもよく、例えば、他の機能部によって他の方法により処理されていてもよい。 Also, each step in the information processing method according to the embodiment of the present disclosure described above does not necessarily have to be processed in the described order. For example, each step may be processed in an appropriately changed order. Also, each step may be partially processed in parallel or individually instead of being processed in chronological order. Furthermore, the processing of each step does not necessarily have to be processed in accordance with the described method, and may be processed by another method by another functional unit, for example.
　上記各実施形態において説明した各処理のうち、自動的に行われるものとして説明した処理の全部または一部を手動的に行うこともでき、あるいは、手動的に行われるものとして説明した処理の全部または一部を公知の方法で自動的に行うこともできる。この他、上記文書中や図面中で示した処理手順、具体的名称、各種のデータやパラメータを含む情報については、特記する場合を除いて任意に変更することができる。例えば、各図に示した各種情報は、図示した情報に限られない。 Of the processes described in each of the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all of the processes described as being performed manually Alternatively, some can be done automatically by known methods. In addition, information including processing procedures, specific names, various data and parameters shown in the above documents and drawings can be arbitrarily changed unless otherwise specified. For example, the various information shown in each drawing is not limited to the illustrated information.
　また、図示した各装置の各構成要素は機能概念的なものであり、必ずしも物理的に図示の如く構成されていることを要しない。すなわち、各装置の分散・統合の具体的形態は図示のものに限られず、その全部または一部を、各種の負荷や使用状況などに応じて、任意の単位で機能的または物理的に分散・統合して構成することができる。 Also, each component of each device illustrated is functionally conceptual and does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution and integration of each device is not limited to the one shown in the figure, and all or part of them can be functionally or physically distributed and integrated in arbitrary units according to various loads and usage conditions. Can be integrated and configured.
　以上、添付図面を参照しながら本開示の好適な実施形態について詳細に説明したが、本開示の技術的範囲はかかる例に限定されない。本開示の技術分野における通常の知識を有する者であれば、請求の範囲に記載された技術的思想の範疇内において、各種の変更例または修正例に想到し得ることは明らかであり、これらについても、当然に本開示の技術的範囲に属するものと了解される。 Although the preferred embodiments of the present disclosure have been described in detail above with reference to the accompanying drawings, the technical scope of the present disclosure is not limited to such examples. It is obvious that a person having ordinary knowledge in the technical field of the present disclosure can conceive of various modifications or modifications within the scope of the technical idea described in the claims. are naturally within the technical scope of the present disclosure.
　また、本明細書に記載された効果は、あくまで説明的または例示的なものであって限定的ではない。つまり、本開示に係る技術は、上記の効果とともに、または上記の効果に代えて、本明細書の記載から当業者には明らかな他の効果を奏しうる。 Also, the effects described in this specification are merely descriptive or exemplary, and are not limiting. In other words, the technology according to the present disclosure can produce other effects that are obvious to those skilled in the art from the description of this specification in addition to or instead of the above effects.
　なお、本技術は以下のような構成も取ることができる。
（１）
　情報処理装置が、
　ユーザの身体に一部に装着されたセンサから得られたセンシングデータを取得することと、
　複数の患者の前記センシングデータ及び治療実績の情報に基づいて、前記複数の患者の中から、前記ユーザの前記センシングデータと類似する前記センシングデータを持つ前記複数の患者を抽出することと、
　抽出した前記複数の患者の治療実績の情報に基づいて、複数の医療機関の中から、前記ユーザに推薦する前記医療機関を決定することと、
　決定された前記医療機関の情報を情報処理端末へ送信することと、
　を含む、情報処理方法。
（２）
　前記センサは、非侵襲のセンサデバイスである、上記（１）に記載の情報処理方法。
（３）
　前記非侵襲のセンサデバイスは、前記ユーザの身体の一部に直接的に装着されて、当該ユーザの心拍、脈拍、血流、血圧、発汗、脳波、呼吸、呼吸量、筋電位、皮膚温度、姿勢、運動状態、歩数、活動量、睡眠状態、睡眠時間、消費カロリー、表情、音声、及び、視線のうちの少なくとも１つを検出する、上記（２）に記載の情報処理方法。
（４）
　前記治療実績の情報は、前記患者が寛解したか否かの情報を含む、上記（２）又は（３）に記載の情報処理方法。
（５）
　前記情報処理装置が、
　抽出した前記複数の患者の治療実績の情報に基づいて、前記各医療機関の推薦度を算出することと、
　前記推薦度を高い前記医療機関を前記ユーザに推薦することと、
　を含む、上記（２）～（４）のいずれか１つに記載の情報処理方法。
（６）
　前記情報処理装置が、精神疾患のための治療のための医療機関を前記ユーザに推薦することを含む、上記（５）に記載の情報処理方法。
（７）
　前記情報処理装置が、
　前記各患者の前記センシングデータの、前記ユーザの前記センシングデータと類似の程度を示す類似度を算出することと、
　算出された前記類似度の高い順に、所定の数の前記患者を抽出することと、
　を含む、上記（５）又は（６）に記載の情報処理方法。
（８）
　前記情報処理装置が、
　前記ユーザが使用するユーザ端末から精神状態に関する問診情報を取得することと、
　複数の医療機関から、複数の患者の前記問診情報及び前記治療実績の情報を取得することと、
　前記各患者の前記問診情報の、前記ユーザの前記問診情報と類似の程度を示す類似度を算出することと、
　算出された前記類似度の高い順に、所定の数の前記患者を抽出することと、
　を含む、上記（７）に記載の情報処理方法。
（９）
　前記情報処理装置が、
　前記ユーザが使用するユーザ端末から精神状態に関する背景情報を取得することと、
　複数の医療機関から、複数の患者の前記背景情報及び前記治療実績の情報を取得することと、
　前記各患者の前記背景情報の、前記ユーザの前記背景情報と類似の程度を示す類似度を算出することと、
　算出された前記類似度の高い順に、所定の数の前記患者を抽出することと、
　を含む、上記（８）に記載の情報処理方法。
（１０）
　前記情報処理装置は、前記ユーザからの入力情報に基づいて、前記所定の数を調整することを含む、上記（７）～（９）のいずれか１つに記載の情報処理方法。
（１１）
　前記情報処理装置が、
　抽出した前記複数の患者における、前記医療機関の受診割合を算出することと、
　算出した受診割合を前記推薦度に設定することと、
　を含む、上記（５）～（１０）のいずれか１つに記載の情報処理方法。
（１２）
　前記情報処理装置が、
　抽出した前記複数の患者における、前記各医療機関による寛解率を算出することと、
　算出した前記寛解率を前記推薦度に設定することと、
　を含む、上記（５）～（１０）のいずれか１つに記載の情報処理方法。
（１３）
　前記情報処理装置が、
　前記複数の医療機関から、
　前記複数の患者の前記センシングデータ、前記問診情報、及び、前記背景情報のうちの少なくとも１つを取得することと、
　当該複数の患者について寛解したか否かの情報を取得することと、
　取得した情報を用いて、当該情報ごとに、前記寛解したか否かの情報との関連性の高さを示す重要度を算出することと、
　を含む、上記（９）に記載の情報処理方法。
（１４）
　前記情報処理装置は、
　算出した重要度に基づき、前記ユーザ端末に対して情報の入力を要求する、
　ことを含む、上記（１３）に記載の情報処理方法。
（１５）
　前記情報処理装置は、算出した重要度に基づき、前記所定の数を調整することを含む、上記（１３）又は（１４）に記載の情報処理方法。
（１６）
　ユーザの身体に一部に装着されたセンサから得られたセンシングデータ、及び、複数の患者の前記センシングデータ及び治療実績の情報を取得する取得部と、
　前記複数の患者の中から、前記ユーザの前記センシングデータと類似する前記センシングデータを持つ前記複数の患者を抽出し、抽出した前記複数の患者の治療実績の情報に基づいて、複数の医療機関の中から、前記ユーザに推薦する前記医療機関を決定する推薦部と、
　を備える、情報処理装置。
（１７）
　前記取得部は、前記複数の医療機関から、前記複数の患者の問診情報、及び、背景情報のうちの少なくとも１つを取得し、且つ、当該複数の患者について寛解したか否かの情報を取得し、
　取得した情報を用いて、当該情報ごとに、前記寛解したか否かの情報との関連性の高さを示す重要度を算出する重要度算出部をさらに備える、
　上記（１６）に記載の情報処理装置。
（１８）
　コンピュータに、
　ユーザの身体に一部に装着されたセンサから得られたセンシングデータを取得する機能と、
　複数の患者の前記センシングデータ及び治療実績の情報に基づいて、前記複数の患者の中から、前記ユーザの前記センシングデータと類似する前記センシングデータを持つ前記複数の患者を抽出する機能と、
　抽出した前記複数の患者の治療実績の情報に基づいて、複数の医療機関の中から、前記ユーザに推薦する前記医療機関を決定する機能と、
　決定された前記医療機関の情報を情報処理端末に送信する機能と、
　を実行させる、プログラム。
（１９）
　ユーザの身体に一部に装着されたセンサと情報処理装置と情報処理端末とを含む情報処理システムであって、
　前記情報処理装置は
　ユーザの身体に一部に装着されたセンサから得られたセンシングデータを取得し、
　複数の患者の前記センシングデータ及び治療実績の情報に基づいて、前記複数の患者の中から、前記ユーザの前記センシングデータと類似する前記センシングデータを持つ前記複数の患者を抽出し、
　抽出した前記複数の患者の治療実績の情報に基づいて、複数の医療機関の中から、前記ユーザに推薦する医療機関を決定し、
　決定された前記医療機関の情報を前記情報処理端末へ送信する、
　情報処理システム。 Note that the present technology can also take the following configuration.
(1)
The information processing device
Acquiring sensing data obtained from a sensor attached to a part of a user's body;
Extracting the plurality of patients having the sensing data similar to the sensing data of the user from among the plurality of patients based on the sensing data and treatment performance information of the plurality of patients;
Determining the medical institution to be recommended to the user from among a plurality of medical institutions based on the extracted information on the treatment results of the plurality of patients;
transmitting the determined information of the medical institution to an information processing terminal;
A method of processing information, comprising:
(2)
The information processing method according to (1) above, wherein the sensor is a non-invasive sensor device.
(3)
The non-invasive sensor device is directly attached to a part of the user's body and measures the user's heart rate, pulse, blood flow, blood pressure, perspiration, electroencephalogram, respiration, respiration volume, myoelectric potential, skin temperature, The information processing method according to (2) above, wherein at least one of posture, motion state, number of steps, amount of activity, sleep state, sleep time, calorie consumption, facial expression, voice, and line of sight is detected.
(4)
The information processing method according to (2) or (3) above, wherein the information on the treatment results includes information on whether or not the patient is in remission.
(5)
The information processing device
calculating the degree of recommendation of each of the medical institutions based on the extracted information on the treatment results of the plurality of patients;
recommending the medical institution with the high recommendation degree to the user;
The information processing method according to any one of (2) to (4) above, including
(6)
The information processing method according to (5) above, wherein the information processing device recommends a medical institution for treatment for mental illness to the user.
(7)
The information processing device
calculating a degree of similarity between the sensing data of each patient and the sensing data of the user;
Extracting a predetermined number of the patients in descending order of the calculated similarity;
The information processing method according to (5) or (6) above, comprising:
(8)
The information processing device
Acquiring interview information about mental state from a user terminal used by the user;
Acquiring the interview information and the treatment performance information of a plurality of patients from a plurality of medical institutions;
calculating a degree of similarity indicating a degree of similarity between the interview information of each patient and the interview information of the user;
Extracting a predetermined number of the patients in descending order of the calculated similarity;
The information processing method according to (7) above, comprising:
(9)
The information processing device
obtaining background information about mental state from a user terminal used by the user;
Acquiring the background information and the treatment performance information of a plurality of patients from a plurality of medical institutions;
calculating a degree of similarity indicating the degree of similarity between the background information of each patient and the background information of the user;
Extracting a predetermined number of the patients in descending order of the calculated similarity;
The information processing method according to (8) above, comprising:
(10)
The information processing method according to any one of (7) to (9) above, wherein the information processing device adjusts the predetermined number based on input information from the user.
(11)
The information processing device
Calculating the consultation rate of the medical institution in the plurality of extracted patients;
Setting the calculated consultation rate as the recommendation degree;
The information processing method according to any one of (5) to (10) above, including
(12)
The information processing device
Calculating the remission rate by each medical institution in the plurality of extracted patients;
setting the calculated remission rate to the recommendation level;
The information processing method according to any one of (5) to (10) above, including
(13)
The information processing device
From the plurality of medical institutions,
Acquiring at least one of the sensing data, the interview information, and the background information of the plurality of patients;
Acquiring information on whether or not the plurality of patients are in remission;
Using the acquired information, for each piece of information, calculating the degree of importance indicating the degree of relevance to the information on whether or not remission has occurred;
The information processing method according to (9) above, comprising:
(14)
The information processing device is
requesting the user terminal to input information based on the calculated importance;
The information processing method according to (13) above, comprising:
(15)
The information processing method according to (13) or (14) above, wherein the information processing device adjusts the predetermined number based on the calculated importance.
(16)
an acquisition unit that acquires sensing data obtained from a sensor attached to a part of a user's body, and information on the sensing data and treatment results of a plurality of patients;
The plurality of patients having the sensing data similar to the sensing data of the user are extracted from the plurality of patients, and based on the extracted treatment performance information of the plurality of patients, the treatment of the plurality of medical institutions is performed. a recommendation unit that determines the medical institution to recommend to the user from among them;
An information processing device.
(17)
The acquisition unit acquires at least one of interview information and background information of the plurality of patients from the plurality of medical institutions, and acquires information as to whether or not the plurality of patients are in remission. death,
Further comprising a degree of importance calculation unit that uses the acquired information to calculate the degree of importance indicating the degree of relevance to the information on whether or not remission has occurred for each piece of information,
The information processing device according to (16) above.
(18)
to the computer,
A function of acquiring sensing data obtained from a sensor attached to a part of the user's body;
A function of extracting the plurality of patients having the sensing data similar to the sensing data of the user from among the plurality of patients based on the sensing data and treatment performance information of the plurality of patients;
a function of determining the medical institution to be recommended to the user from among a plurality of medical institutions based on the extracted information on the treatment performance of the plurality of patients;
a function of transmitting information on the determined medical institution to an information processing terminal;
The program that causes the to run.
(19)
An information processing system including a sensor attached to a part of a user's body, an information processing device, and an information processing terminal,
The information processing device acquires sensing data obtained from a sensor attached to a part of a user's body,
extracting the plurality of patients having the sensing data similar to the sensing data of the user from among the plurality of patients based on the sensing data and treatment performance information of the plurality of patients;
determining a medical institution to recommend to the user from among a plurality of medical institutions based on the extracted information on the treatment results of the plurality of patients;
transmitting the determined information of the medical institution to the information processing terminal;
Information processing system.
　　１０　　情報処理システム
　　１００　　ウェアラブルデバイス
　　１１０、２１０、３１０　　入力部
　　１２０、２２０、３２０　　出力部
　　１３０、２３０　　制御部
　　１４０　　センサ部
　　１５０、２５０、３５０　　通信部
　　１６０、２６０、３６０　　記憶部
　　１７０　　バンド部
　　１８０　　制御ユニット
　　２００　　ユーザ端末
　　３００　　サーバ
　　３３０　　処理部
　　３３２　　取得部
　　３３４　　重要度算出部
　　３３６　　推薦度算出部
　　３３８　　出力制御部
　　３６２、３６２ａ、３６２ｂ、３６２ｎ　　医療機関情報テーブル
　　３６４　　重要度ランキングテーブル
　　３６６　　ユーザ情報テーブル
　　４００　　医療機関端末
　　５００　　ネットワーク
　　６００　　質問
　　６０２　　ボタン 10 information processing system 100 wearable device 110, 210, 310 input section 120, 220, 320 output section 130, 230 control section 140 sensor section 150, 250, 350 communication section 160, 260, 360 storage section 170 band section 180 control unit 200 User terminal 300 Server 330 Processing unit 332 Acquisition unit 334 Importance calculation unit 336 Recommendation calculation unit 338 Output control unit 362, 362a, 362b, 362n Medical institution information table 364 Importance ranking table 366 User information table 400 Medical institution terminal 500 Network 600 question 602 button

Claims (19)

  1. 　情報処理装置が、
    　ユーザの身体に一部に装着されたセンサから得られたセンシングデータを取得することと、
    　複数の患者の前記センシングデータ及び治療実績の情報に基づいて、前記複数の患者の中から、前記ユーザの前記センシングデータと類似する前記センシングデータを持つ前記複数の患者を抽出することと、
    　抽出した前記複数の患者の治療実績の情報に基づいて、複数の医療機関の中から、前記ユーザに推薦する前記医療機関を決定することと、
    　決定された前記医療機関の情報を情報処理端末へ送信することと、
    　を含む、情報処理方法。     The information processing device
    Acquiring sensing data obtained from a sensor attached to a part of a user's body;
    Extracting the plurality of patients having the sensing data similar to the sensing data of the user from among the plurality of patients based on the sensing data and treatment performance information of the plurality of patients;
    Determining the medical institution to be recommended to the user from among a plurality of medical institutions based on the extracted information on the treatment results of the plurality of patients;
    transmitting the determined information of the medical institution to an information processing terminal;
    A method of processing information, comprising:
  2. 　前記センサは、非侵襲のセンサデバイスである、請求項１に記載の情報処理方法。 The information processing method according to claim 1, wherein the sensor is a non-invasive sensor device.
  3. 　前記非侵襲のセンサデバイスは、前記ユーザの身体の一部に直接的に装着されて、当該ユーザの心拍、脈拍、血流、血圧、発汗、脳波、呼吸、呼吸量、筋電位、皮膚温度、姿勢、運動状態、歩数、活動量、睡眠状態、睡眠時間、消費カロリー、表情、音声、及び、視線のうちの少なくとも１つを検出する、請求項２に記載の情報処理方法。 The non-invasive sensor device is directly attached to a part of the user's body and measures the user's heart rate, pulse, blood flow, blood pressure, perspiration, electroencephalogram, respiration, respiration volume, myoelectric potential, skin temperature, 3. The information processing method according to claim 2, wherein at least one of posture, motion state, number of steps, amount of activity, sleep state, sleep time, calorie consumption, facial expression, voice, and line of sight is detected.
  4. 　前記治療実績の情報は、前記患者が寛解したか否かの情報を含む、請求項２に記載の情報処理方法。 The information processing method according to claim 2, wherein the information on the treatment results includes information on whether or not the patient is in remission.
  5. 　前記情報処理装置が、
    　抽出した前記複数の患者の治療実績の情報に基づいて、前記各医療機関の推薦度を算出することと、
    　前記推薦度を高い前記医療機関を前記ユーザに推薦することと、
    　を含む、請求項２に記載の情報処理方法。     The information processing device
    calculating the degree of recommendation of each of the medical institutions based on the extracted information on the treatment results of the plurality of patients;
    recommending the medical institution with the high recommendation degree to the user;
    The information processing method according to claim 2, comprising:
  6. 　前記情報処理装置が、精神疾患のための治療のための医療機関を前記ユーザに推薦することを含む、請求項５に記載の情報処理方法。 The information processing method according to claim 5, wherein the information processing device recommends a medical institution for treatment of mental illness to the user.
  7. 　前記情報処理装置が、
    　前記各患者の前記センシングデータの、前記ユーザの前記センシングデータと類似の程度を示す類似度を算出することと、
    　算出された前記類似度の高い順に、所定の数の前記患者を抽出することと、
    　を含む、請求項５に記載の情報処理方法。     The information processing device
    calculating a degree of similarity between the sensing data of each patient and the sensing data of the user;
    Extracting a predetermined number of the patients in descending order of the calculated similarity;
    The information processing method according to claim 5, comprising:
  8. 　前記情報処理装置が、
    　前記ユーザが使用するユーザ端末から精神状態に関する問診情報を取得することと、
    　複数の医療機関から、複数の患者の前記問診情報及び前記治療実績の情報を取得することと、
    　前記各患者の前記問診情報の、前記ユーザの前記問診情報と類似の程度を示す類似度を算出することと、
    　算出された前記類似度の高い順に、所定の数の前記患者を抽出することと、
    　を含む、請求項７に記載の情報処理方法。     The information processing device
    Acquiring interview information about mental state from a user terminal used by the user;
    Acquiring the interview information and the treatment performance information of a plurality of patients from a plurality of medical institutions;
    calculating a degree of similarity indicating a degree of similarity between the interview information of each patient and the interview information of the user;
    Extracting a predetermined number of the patients in descending order of the calculated similarity;
    The information processing method according to claim 7, comprising:
  9. 　前記情報処理装置が、
    　前記ユーザが使用するユーザ端末から精神状態に関する背景情報を取得することと、
    　複数の医療機関から、複数の患者の前記背景情報及び前記治療実績の情報を取得することと、
    　前記各患者の前記背景情報の、前記ユーザの前記背景情報と類似の程度を示す類似度を算出することと、
    　算出された前記類似度の高い順に、所定の数の前記患者を抽出することと、
    　を含む、請求項８に記載の情報処理方法。     The information processing device
    obtaining background information about mental state from a user terminal used by the user;
    Acquiring the background information and the treatment performance information of a plurality of patients from a plurality of medical institutions;
    calculating a degree of similarity indicating the degree of similarity between the background information of each patient and the background information of the user;
    Extracting a predetermined number of the patients in descending order of the calculated similarity;
    The information processing method according to claim 8, comprising:
  10. 　前記情報処理装置は、前記ユーザからの入力情報に基づいて、前記所定の数を調整することを含む、請求項７に記載の情報処理方法。 The information processing method according to claim 7, wherein the information processing device adjusts the predetermined number based on input information from the user.
  11. 　前記情報処理装置が、
    　抽出した前記複数の患者における、前記医療機関の受診割合を算出することと、
    　算出した受診割合を前記推薦度に設定することと、
    　を含む、請求項５に記載の情報処理方法。     The information processing device
    Calculating the consultation rate of the medical institution in the plurality of extracted patients;
    Setting the calculated consultation rate as the recommendation degree;
    The information processing method according to claim 5, comprising:
  12. 　前記情報処理装置が、
    　抽出した前記複数の患者における、前記各医療機関による寛解率を算出することと、
    　算出した前記寛解率を前記推薦度に設定することと、
    　を含む、請求項５に記載の情報処理方法。     The information processing device
    Calculating the remission rate by each medical institution in the plurality of extracted patients;
    setting the calculated remission rate to the recommendation level;
    The information processing method according to claim 5, comprising:
  13. 　前記情報処理装置が、
    　前記複数の医療機関から、
    　前記複数の患者の前記センシングデータ、前記問診情報、及び、前記背景情報のうちの少なくとも１つを取得することと、
    　当該複数の患者について寛解したか否かの情報を取得することと、
    　取得した情報を用いて、当該情報ごとに、前記寛解したか否かの情報との関連性の高さを示す重要度を算出することと、
    　を含む、請求項９に記載の情報処理方法。     The information processing device
    From the plurality of medical institutions,
    Acquiring at least one of the sensing data, the interview information, and the background information of the plurality of patients;
    Acquiring information on whether or not the plurality of patients are in remission;
    Using the acquired information, for each piece of information, calculating the degree of importance indicating the degree of relevance to the information on whether or not remission has occurred;
    The information processing method according to claim 9, comprising:
  14. 　前記情報処理装置は、
    　算出した重要度に基づき、前記ユーザ端末に対して情報の入力を要求する、
    　ことを含む、請求項１３に記載の情報処理方法。     The information processing device is
    requesting the user terminal to input information based on the calculated importance;
    14. The information processing method according to claim 13, comprising:
  15. 　前記情報処理装置は、算出した重要度に基づき、前記所定の数を調整することを含む、請求項１３に記載の情報処理方法。 The information processing method according to claim 13, wherein the information processing device adjusts the predetermined number based on the calculated importance.
  16. 　ユーザの身体に一部に装着されたセンサから得られたセンシングデータ、及び、複数の患者の前記センシングデータ及び治療実績の情報を取得する取得部と、
    　前記複数の患者の中から、前記ユーザの前記センシングデータと類似する前記センシングデータを持つ前記複数の患者を抽出し、抽出した前記複数の患者の治療実績の情報に基づいて、複数の医療機関の中から、前記ユーザに推薦する前記医療機関を決定する推薦部と、
    　を備える、情報処理装置。     an acquisition unit that acquires sensing data obtained from a sensor attached to a part of a user's body, and information on the sensing data and treatment results of a plurality of patients;
    The plurality of patients having the sensing data similar to the sensing data of the user are extracted from the plurality of patients, and based on the extracted treatment performance information of the plurality of patients, the treatment of the plurality of medical institutions is performed. a recommendation unit that determines the medical institution to recommend to the user from among them;
    An information processing device.
  17. 　前記取得部は、前記複数の医療機関から、前記複数の患者の問診情報、及び、背景情報のうちの少なくとも１つを取得し、且つ、当該複数の患者について寛解したか否かの情報を取得し、
    　取得した情報を用いて、当該情報ごとに、前記寛解したか否かの情報との関連性の高さを示す重要度を算出する重要度算出部をさらに備える、
    　請求項１６に記載の情報処理装置。     The acquisition unit acquires at least one of interview information and background information of the plurality of patients from the plurality of medical institutions, and acquires information as to whether or not the plurality of patients are in remission. death,
    Further comprising a degree of importance calculation unit that uses the acquired information to calculate the degree of importance indicating the degree of relevance to the information on whether or not remission has occurred for each piece of information,
    The information processing apparatus according to claim 16.
  18. 　コンピュータに、
    　ユーザの身体に一部に装着されたセンサから得られたセンシングデータを取得する機能と、
    　複数の患者の前記センシングデータ及び治療実績の情報に基づいて、前記複数の患者の中から、前記ユーザの前記センシングデータと類似する前記センシングデータを持つ前記複数の患者を抽出する機能と、
    　抽出した前記複数の患者の治療実績の情報に基づいて、複数の医療機関の中から、前記ユーザに推薦する前記医療機関を決定する機能と、
    　決定された前記医療機関の情報を情報処理端末に送信する機能と、
    　を実行させる、プログラム。     to the computer,
    A function of acquiring sensing data obtained from a sensor attached to a part of the user's body;
    A function of extracting the plurality of patients having the sensing data similar to the sensing data of the user from among the plurality of patients based on the sensing data and treatment performance information of the plurality of patients;
    a function of determining the medical institution to be recommended to the user from among a plurality of medical institutions based on the extracted information on the treatment performance of the plurality of patients;
    a function of transmitting information on the determined medical institution to an information processing terminal;
    The program that causes the to run.
  19. 　ユーザの身体に一部に装着されたセンサと情報処理装置と情報処理端末とを含む情報処理システムであって、
    　前記情報処理装置は
    　ユーザの身体に一部に装着されたセンサから得られたセンシングデータを取得し、
    　複数の患者の前記センシングデータ及び治療実績の情報に基づいて、前記複数の患者の中から、前記ユーザの前記センシングデータと類似する前記センシングデータを持つ前記複数の患者を抽出し、
    　抽出した前記複数の患者の治療実績の情報に基づいて、複数の医療機関の中から、前記ユーザに推薦する医療機関を決定し、
    　決定された前記医療機関の情報を前記情報処理端末へ送信する、
    　情報処理システム。     An information processing system including a sensor attached to a part of a user's body, an information processing device, and an information processing terminal,
    The information processing device acquires sensing data obtained from a sensor attached to a part of a user's body,
    extracting the plurality of patients having the sensing data similar to the sensing data of the user from among the plurality of patients based on the sensing data and treatment performance information of the plurality of patients;
    determining a medical institution to recommend to the user from among a plurality of medical institutions based on the extracted information on the treatment results of the plurality of patients;
    transmitting the determined information of the medical institution to the information processing terminal;
    Information processing system.
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