CN117373658B - Data processing-based auxiliary diagnosis and treatment system and method for depression - Google Patents
Data processing-based auxiliary diagnosis and treatment system and method for depression Download PDFInfo
- Publication number
- CN117373658B CN117373658B CN202311675914.3A CN202311675914A CN117373658B CN 117373658 B CN117373658 B CN 117373658B CN 202311675914 A CN202311675914 A CN 202311675914A CN 117373658 B CN117373658 B CN 117373658B
- Authority
- CN
- China
- Prior art keywords
- patient
- diagnosed
- inquiry
- sample
- test question
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000003745 diagnosis Methods 0.000 title claims abstract description 80
- 238000000034 method Methods 0.000 title claims abstract description 33
- 201000010099 disease Diseases 0.000 claims abstract description 23
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims abstract description 23
- 230000008451 emotion Effects 0.000 claims description 69
- 210000004556 brain Anatomy 0.000 claims description 22
- 230000002996 emotional effect Effects 0.000 claims description 21
- 208000020401 Depressive disease Diseases 0.000 claims description 10
- 238000001514 detection method Methods 0.000 claims description 9
- 239000003814 drug Substances 0.000 claims description 2
- 238000013528 artificial neural network Methods 0.000 description 10
- 238000000605 extraction Methods 0.000 description 8
- 208000024714 major depressive disease Diseases 0.000 description 7
- 238000009098 adjuvant therapy Methods 0.000 description 6
- 230000007935 neutral effect Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 230000000306 recurrent effect Effects 0.000 description 4
- 238000002564 cardiac stress test Methods 0.000 description 3
- 230000003001 depressive effect Effects 0.000 description 3
- 230000000747 cardiac effect Effects 0.000 description 2
- 125000004122 cyclic group Chemical group 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 230000006403 short-term memory Effects 0.000 description 2
- 206010010144 Completed suicide Diseases 0.000 description 1
- 206010021137 Hypovolaemia Diseases 0.000 description 1
- 201000009916 Postpartum depression Diseases 0.000 description 1
- 208000012826 adjustment disease Diseases 0.000 description 1
- 230000004596 appetite loss Effects 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000003370 grooming effect Effects 0.000 description 1
- 208000019017 loss of appetite Diseases 0.000 description 1
- 235000021266 loss of appetite Nutrition 0.000 description 1
- 208000015238 neurotic disease Diseases 0.000 description 1
- 239000000546 pharmaceutical excipient Substances 0.000 description 1
- 208000020016 psychiatric disease Diseases 0.000 description 1
- 208000019116 sleep disease Diseases 0.000 description 1
- 230000003860 sleep quality Effects 0.000 description 1
- 208000020685 sleep-wake disease Diseases 0.000 description 1
- 208000024891 symptom Diseases 0.000 description 1
Classifications
-
- 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
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
-
- 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
- G16H10/20—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
-
- 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
- G16H20/70—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mental therapies, e.g. psychological therapy or autogenous training
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Medical Informatics (AREA)
- Public Health (AREA)
- Epidemiology (AREA)
- Primary Health Care (AREA)
- General Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Databases & Information Systems (AREA)
- Pathology (AREA)
- Data Mining & Analysis (AREA)
- Child & Adolescent Psychology (AREA)
- Developmental Disabilities (AREA)
- Hospice & Palliative Care (AREA)
- Psychiatry (AREA)
- Psychology (AREA)
- Social Psychology (AREA)
- Medical Treatment And Welfare Office Work (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
The invention relates to a depression auxiliary diagnosis and treatment system and method based on data processing, and relates to the technical field of data processing, wherein the system comprises: the question bank building module is used for obtaining voice question records of a plurality of historical patients and generating a question bank; the sample acquisition module is used for acquiring a plurality of sample inquiry results based on the inquiry question bank; the data processing module is used for processing a plurality of sample inquiry results and establishing a test question association map; the disease inquiry module comprises a multimedia inquiry component and an information acquisition component, and is used for inquiring a patient to be diagnosed based on an inquiry question library and a test question association map and acquiring feedback information of the patient to be diagnosed; the disease diagnosis module is used for generating an auxiliary diagnosis result of the patient to be diagnosed based on feedback information of the patient to be diagnosed; the auxiliary treatment module is used for generating an auxiliary treatment scheme and has the advantages of realizing the automation of auxiliary diagnosis and treatment of the depression and improving the diagnosis and treatment effect of the depression.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a depression auxiliary diagnosis and treatment system and method based on data processing.
Background
Depression is a common psychological disorder that is manifested mainly by a series of symptoms such as hypomnesis, slow thinking, hypovolemia, sleep disorder, and loss of appetite. The detection and treatment of the depression face a plurality of problems, such as low identification accuracy rate of the depression, lack of medical resources, lack of trained health care personnel and the like, so that the introduction of artificial intelligence related technologies is urgently needed, the identification accuracy rate of the depression is improved, the absence of doctors in the medical industry is made up, and the detection and prediction efficiency of the depression is improved. In addition, in the current depression diagnosis and treatment process, a large amount of repeated inquiry work based on scales exists, so that the depression diagnosis and treatment efficiency is lower, and the experience of patients is poor.
Therefore, it is necessary to provide a data processing-based auxiliary diagnosis and treatment system and method for depression, which are used for providing reference results for diagnosis and treatment of depression and solving the problems of time consumption and low efficiency in the diagnosis and treatment process of depression.
Disclosure of Invention
The invention provides a depression auxiliary diagnosis and treatment system based on data processing, which comprises the following components: the system comprises a question bank establishing module, a question bank processing module and a question bank processing module, wherein the question bank establishing module is used for acquiring voice question records of a plurality of historical patients and generating a question bank, and the question bank comprises a plurality of psychological test questions; the sample acquisition module is used for carrying out inquiry on a plurality of sample patients based on the inquiry question library to acquire a plurality of sample inquiry results; the data processing module is used for processing the plurality of sample inquiry results and establishing a test question association map; the disease inquiry module comprises a multimedia inquiry module and an information acquisition module, wherein the multimedia inquiry module is used for inquiring a patient to be diagnosed based on the inquiry question bank and the test question association map, and the information acquisition module is used for acquiring feedback information of the patient to be diagnosed in the process of inquiring the patient to be diagnosed by the multimedia inquiry module; the disease diagnosis module is used for generating an auxiliary diagnosis result of the patient to be diagnosed based on the feedback information of the patient to be diagnosed; and the auxiliary treatment module is used for generating an auxiliary treatment scheme based on the diagnosis result of the patient to be diagnosed.
Further, the question bank establishing module obtains voice question records of a plurality of historical patients, and generates a question bank, including: acquiring voice inquiry records of a plurality of historical patients, determining the occurrence frequency of a plurality of keywords, and determining a plurality of target keywords; generating a plurality of first psychological inquiry test questions based on the plurality of target keywords and the voice inquiry records of the plurality of historical patients, wherein the plurality of psychological test questions comprise the plurality of first psychological inquiry test questions; generating a plurality of second cardiac diagnostic test questions based on the plurality of first cardiac diagnostic test questions through a test question generation model, wherein the plurality of psychological test questions comprises the plurality of second cardiac diagnostic test questions.
Further, the data processing module processes the plurality of sample inquiry results to establish a test question association map, including: clustering the plurality of sample patients based on the disease information of the sample patients to generate a plurality of sample patient clusters; and classifying the multi-channel psychological test questions based on sample inquiry results of each sample patient included in each sample patient cluster, determining a plurality of test question clusters, and establishing a test question association map, wherein one test question cluster corresponds to one depression level.
Furthermore, the multimedia inquiry module performs inquiry on the patient to be diagnosed based on the inquiry question library and the test question association map, including: acquiring relevant information of the patient to be diagnosed, determining an initial depression level corresponding to the patient to be diagnosed, and taking the initial depression level as a current depression level; repeatedly executing the multimedia inquiry module to inquire the patient to be diagnosed based on the test question cluster corresponding to the current depression level, updating the current depression level based on the feedback information of the patient to be diagnosed on the test question cluster corresponding to the current depression level and the test question association map acquired by the information acquisition module, and inquiring the patient to be diagnosed based on the updated test question cluster corresponding to the current depression level until a preset condition is met.
Further, the multimedia inquiry module updates the current depression level based on the feedback information of the to-be-diagnosed patient on the test question cluster corresponding to the current depression level and the test question association map, which are acquired by the information acquisition module, and the multimedia inquiry module includes: determining the feedback emotion type of the patient to be diagnosed on the test question cluster corresponding to the current depression level based on the feedback information of the patient to be diagnosed on the test question cluster corresponding to the current depression level, which is acquired by the information acquisition component; updating the current depression level based on the feedback emotion type of the patient to be diagnosed on the test question cluster corresponding to the current depression level and the test question association map.
Still further, the information acquisition component at least comprises a voice acquisition device, an image acquisition device, an electroencephalogram acquisition device and a sitting posture acquisition device, wherein the voice acquisition device is used for acquiring voice feedback information of the patient to be diagnosed on a test question cluster corresponding to the current depression level, the image acquisition device is used for acquiring image feedback information of the patient to be diagnosed on the test question cluster corresponding to the current depression level, the electroencephalogram acquisition device is used for acquiring brain wave feedback information of the patient to be diagnosed on the test question cluster corresponding to the current depression level, and the sitting posture acquisition device is used for acquiring pose feedback information of the patient to be diagnosed on the test question cluster corresponding to the current depression level; the sitting posture collection device comprises a detection chair, and a cushion pressure sensor, a chair back pressure sensor and an armrest pressure sensor which are arranged on the detection chair.
Further, the multimedia inquiry module determines a feedback emotion type of the patient to be diagnosed for the test question cluster corresponding to the current depression level based on the feedback information of the patient to be diagnosed for the test question cluster corresponding to the current depression level acquired by the information acquisition module, and the multimedia inquiry module includes: extracting voice feedback information of the patient to be diagnosed on the test question cluster corresponding to the current depression level, and determining an initial feedback emotion type of the patient to be diagnosed on the test question cluster corresponding to the current depression level; determining the first emotion authenticity of the initial feedback emotion type based on the voice feedback information of the patient to be diagnosed on the test question cluster corresponding to the current depression level; determining second emotion authenticity of the initial feedback emotion type based on image feedback information of the patient to be diagnosed on the test question cluster corresponding to the current depression level; determining third emotion authenticity of the initial feedback emotion type based on brain wave feedback information of the patient to be diagnosed on the test question cluster corresponding to the current depression level; determining fourth emotion authenticity of the initial feedback emotion type based on pose feedback information of the patient to be diagnosed on the test question cluster corresponding to the current depression level; determining the authenticity of the initial feedback emotion type based on the first emotional authenticity, the second emotional authenticity, the third emotional authenticity and the fourth emotional authenticity.
Further, the disease diagnosis module generates an auxiliary diagnosis result of the patient to be diagnosed based on the feedback information of the patient to be diagnosed, including: establishing a plurality of sample patient diagnostic portraits based on the relevant information of the plurality of sample patients and the plurality of sample inquiry results; based on the related information of the patient to be diagnosed and the feedback information of the patient to be diagnosed, establishing a patient portrait corresponding to the patient to be diagnosed; and determining a target sample patient diagnostic portrait from the plurality of sample patient diagnostic portraits based on the patient portraits corresponding to the patient to be diagnosed, and generating an auxiliary diagnostic result of the patient to be diagnosed based on the target sample patient diagnostic portrait.
Still further, the auxiliary treatment module generates an auxiliary treatment scheme based on the diagnosis result of the patient to be diagnosed, including: generating an auxiliary treatment scheme of the patient to be diagnosed based on the target sample patient diagnostic portrait, wherein the auxiliary treatment scheme at least comprises a psychological dredging auxiliary treatment scheme, a medicine auxiliary treatment scheme and/or a transcranial auxiliary treatment scheme.
The invention provides a data processing-based auxiliary diagnosis and treatment method for depression, which comprises the following steps of: acquiring voice inquiry records of a plurality of historical patients, and generating an inquiry question library, wherein the inquiry question library comprises a plurality of psychological test questions; based on the inquiry question library, carrying out inquiry on a plurality of sample patients to obtain a plurality of sample inquiry results; processing the multiple sample inquiry results and establishing a test question association map; using a multimedia inquiry assembly to inquire a patient to be diagnosed based on the inquiry question library and the test question association map; acquiring feedback information of the patient to be diagnosed by using an information acquisition component in the process of inquiring the patient to be diagnosed by using the multimedia inquiry component; generating an auxiliary diagnosis result of the patient to be diagnosed based on the feedback information of the patient to be diagnosed; and generating an auxiliary treatment scheme based on the diagnosis result of the patient to be diagnosed.
Compared with the prior art, the data processing-based auxiliary diagnosis and treatment system and method for depression provided by the specification have the following beneficial effects:
1. the method comprises the steps of establishing a question bank and carrying out question diagnosis on a plurality of sample patients, acquiring a plurality of sample question results, and completing the establishment of a test question association map, further, completing the automatic question diagnosis of the patient to be diagnosed based on the question bank and the test question association map through a multimedia question component, and automatically acquiring feedback information of the patient to be diagnosed through an information acquisition component, so that reference results of diagnosis and treatment of depression can be automatically generated, participation by manpower is not needed, and the problems of time consumption and low efficiency in the diagnosis and treatment process of depression are solved;
2. generating a plurality of second cardiac inquiry test questions based on the plurality of first cardiac inquiry test questions through the test question generation model, reducing the task amount of acquiring the psychological inquiry test questions in the early stage, establishing a sufficient question bank for the inquiry of the patient to be diagnosed, and improving the quality of the follow-up auxiliary diagnosis results of the patient to be diagnosed;
3. classifying a plurality of psychological test questions based on sample inquiry results of each sample patient included in the sample patient cluster, determining a plurality of test question clusters, and establishing a test question association map, so that the intelligence of subsequent automatic inquiry is realized, and the situation that the same or similar psychological test questions are repeatedly carried out on patients to be diagnosed is avoided;
4. through voice acquisition equipment, image acquisition equipment, brain wave acquisition equipment and position of sitting collection equipment, gather multidimensional information, confirm the emotion authenticity of initial feedback emotion type more accurately from the multidimensional angle, avoided the acquisition of invalid test result, improved depression auxiliary diagnosis's intelligence and accuracy.
Drawings
The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:
FIG. 1 is a block diagram of a data processing-based depression assisted diagnosis and treatment system according to an embodiment of the present application;
FIG. 2 is a flow chart illustrating the generation of a question bank in one embodiment of the present application;
FIG. 3 is a schematic structural view of an information acquisition assembly according to an embodiment of the present application;
FIG. 4 is a flow chart illustrating the determination of feedback emotion type in an embodiment of the present application;
FIG. 5 is a flow chart illustrating the generation of auxiliary diagnostic results for a patient to be diagnosed in an embodiment of the present application;
FIG. 6 is a flow chart of a data processing-based method of assisted diagnosis and treatment of depression, as shown in an embodiment of the present application;
FIG. 7 is a schematic diagram of a multi-channel psychological test question according to one embodiment of the present application.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below.
Fig. 1 is a block diagram of a data processing-based auxiliary diagnosis and treatment system for depression, and as shown in fig. 1, the data processing-based auxiliary diagnosis and treatment system for depression may include a question bank building module, a sample acquiring module, a data processing module, a condition inquiring module, a condition diagnosing module and an auxiliary treatment module. The respective modules are described in detail below with reference to the accompanying drawings.
The question bank building module can be used for obtaining voice question records of a plurality of historical patients and generating a question bank.
The question bank may include a plurality of psychological test questions, and the plurality of psychological test questions may include a plurality of types, for example, selection questions, and the like. By way of example only, FIG. 7 is a schematic illustration of a multi-channel psychological test question shown in an embodiment of the present application, as shown in FIG. 7, which may include selection questions.
FIG. 2 is a flowchart illustrating the generation of a question bank according to one embodiment of the present application, as shown in FIG. 2, and in some embodiments, the question bank establishment module obtains voice question records of a plurality of historical patients, and generates a question bank, including:
acquiring voice inquiry records of a plurality of historical patients, determining the occurrence frequency of a plurality of keywords, and determining a plurality of target keywords, wherein the voice inquiry records of the historical patients can be real voice inquiry records of doctors and depression patients, and the target keywords can be keywords with the occurrence frequency larger than a preset occurrence frequency threshold;
generating a plurality of first psychological inquiry test questions based on a plurality of target keywords and a plurality of voice inquiry records of historical patients, wherein the plurality of psychological test questions comprise a plurality of first psychological inquiry test questions;
and generating a plurality of second psychological inquiry test questions based on the plurality of first psychological inquiry test questions through a test question generation model, wherein the plurality of psychological inquiry test questions comprise the plurality of second psychological inquiry test questions, and the test question generation model can generate an impedance network (Generative Adversarial Nets, GAN) model.
Specifically, the question bank establishment module may first establish a keyword word bank, where the keyword word bank may include a plurality of keywords. For each voice inquiry record, extracting keywords in the voice inquiry record based on a keyword word stock through a keyword extraction model, counting the occurrence frequency of each keyword, and determining a plurality of target keywords. The keyword extraction model may be a machine learning model such as an artificial neural network (Artificial Neural Network, ANN) model, a cyclic neural network (Recurrent Neural Networks, RNN) model, a Long Short-Term Memory (LSTM) model, a bi-directional cyclic neural network (BRNN) model, etc.
The sample acquisition module can be used for carrying out inquiry on a plurality of sample patients based on the inquiry question bank to acquire a plurality of sample inquiry results.
The sample patient may be a patient diagnosed by a doctor as suffering from depression. It will be appreciated that the severity or type of depression suffered by different sample patients may vary. For example, it may be major depressive disorder, postpartum depression, secondary depression, reactive depression, etc.
The data processing module can be used for processing a plurality of sample inquiry results and establishing a test question association map.
In some embodiments, the data processing module processes the multiple sample inquiry results to establish a test question association map, including:
clustering a plurality of sample patients based on disease information of the sample patients to generate a plurality of sample patient clusters;
for each sample patient cluster, classifying a plurality of psychological test questions based on sample inquiry results of each sample patient included in the sample patient cluster, determining a plurality of test question clusters, and establishing a test question association map, wherein one test question cluster corresponds to one depression level.
Specifically, for any two sample patients, the data processing module may calculate the disease similarity between the sample patients according to the diagnosis results of the two sample patients, and cluster the plurality of sample patients based on the disease similarity between any two sample patients, so as to generate a plurality of sample patient clusters. For example, mild depressive patients are clustered into one sample patient cluster, moderate depressive patients are clustered into one sample patient cluster, and severe depressive patients are clustered into one sample patient cluster.
In some embodiments, the data processing module may cluster the plurality of sample patients based on the similarity of conditions between any two sample patients based on the following procedure to generate a plurality of sample patient clusters:
firstly, randomly selecting m sample patients from a plurality of sample patients as initial clustering centers, finding out a clustering center with the largest disease similarity between each other sample patient and the other sample patient, and distributing the other sample patient to a sample patient cluster corresponding to the clustering center with the largest disease similarity between the other sample patients. And then, based on the disease similarity between any two sample patients in each sample patient cluster, determining the sample patient with the largest average value of the corresponding disease similarity as a new cluster center, and carrying out the next iteration until the cluster center is not changed or the maximum iteration number is reached.
For example, the mean value of the disease similarity for a sample patient may be determined based on the disease similarity between any two sample patients in each sample patient cluster according to the following formula:
wherein,the average value of the disease similarity corresponding to the jth sample patient in the ith sample patient cluster,for the disease similarity between the jth sample patient in the ith sample patient cluster and the nth other sample patient in the ith sample patient cluster, < >>Is the total number of sample patients in the ith sample patient cluster.
In some embodiments, the data processing module may determine feedback emotion types of different types of sample patient clusters for each psychological test question based on sample interrogation results of each sample patient included in the sample patient cluster, classify the psychological test questions, and determine a plurality of test question clusters.
For example, the proportion of feedback emotion types of each sample patient cluster to each psychological test question is calculated, and the multichannel psychological test questions are classified. For example only, for psychological test question a,80% of the sample patients included in the sample patient cluster corresponding to major depression are negative for the type of feedback emotion of psychological test question a, 70% of the sample patients included in the sample patient cluster corresponding to major depression are negative or neutral for the type of feedback emotion of psychological test question a, 30% of the sample patients included in the sample patient cluster corresponding to major depression are negative or neutral for the type of feedback emotion of psychological test question a, and psychological test question a may be assigned to the test question cluster corresponding to minor depression. For example, for the psychological test question B, the sample patients included in the sample patient cluster corresponding to 80% of the major depression are negative for the feedback emotion type of the psychological test question a, the sample patients included in the sample patient cluster corresponding to 50% of the major depression are negative or neutral for the feedback emotion type of the psychological test question a, and the sample patients included in the sample patient cluster corresponding to 10% of the major depression are negative or neutral for the feedback emotion type of the psychological test question a, the psychological test question a may be allocated to the test question cluster corresponding to the moderate depression.
For each test question cluster, the test question clusters can be divided into a plurality of sub-clusters according to the types of psychological test questions included in the test question cluster. For example, an emotional state class sub-cluster, an hobby class sub-cluster, a sleep quality class sub-cluster, an energy spirit class sub-cluster, a suicide risk class sub-cluster, and the like.
The test question association graph can comprise a plurality of first-level nodes, wherein one first-level node corresponds to one test question cluster, a connection relation is established between any two adjacent first-level nodes according to depression levels corresponding to the two test question clusters, the test question association graph can also comprise a plurality of second-level nodes, one second-level node corresponds to one sub-cluster, and the first-level nodes are connected with the second-level nodes corresponding to all the sub-clusters included in the test question cluster corresponding to the first-level node through edges.
The condition inquiry module can comprise a multimedia inquiry module and an information acquisition module.
The multimedia inquiry assembly is used for inquiring the patient to be diagnosed based on the inquiry question library and the test question association map. For example, the multimedia inquiry assembly may include a controller, a display screen and a sound box, where the controller is configured to control the display screen to display psychological test questions based on the inquiry question bank and the test question association map, and/or control the sound box to send out inquiry voice to inquire the patient to be diagnosed.
The information acquisition component is used for acquiring feedback information of the patient to be diagnosed in the process of inquiring the patient to be diagnosed by the multimedia inquiry component.
Fig. 3 is a schematic structural diagram of an information collection assembly shown in an embodiment of the present application, as shown in fig. 3, in some embodiments, the information collection assembly at least includes a voice collection device (e.g., a microphone), an image collection device (e.g., a high-definition camera), a brain wave collection device, and a sitting posture collection device, where the voice collection device is used for collecting voice feedback information of a test question cluster corresponding to a current depression level of a patient to be diagnosed, the image collection device is used for collecting image feedback information of the test question cluster corresponding to the current depression level of the patient to be diagnosed, the brain wave collection device is used for collecting brain wave feedback information of the test question cluster corresponding to the current depression level of the patient to be diagnosed, and the sitting posture collection device is used for collecting pose feedback information of the test question cluster corresponding to the current depression level of the patient to be diagnosed. The sitting posture collection device comprises a detection chair and a cushion pressure sensor, a chair back pressure sensor and an armrest pressure sensor which are arranged on the detection chair, wherein the cushion pressure sensor, the chair back pressure sensor and the armrest pressure sensor can all comprise a plurality of piezoelectric sensors. It can be understood that in the process of the multimedia inquiry module for inquiring the patient to be diagnosed, the patient to be diagnosed needs to be seated on the detection chair.
In some embodiments, the multimedia inquiry module performs inquiry on the patient to be diagnosed based on the inquiry question bank and the test question association map, including:
acquiring relevant information of a patient to be diagnosed, determining an initial depression level corresponding to the patient to be diagnosed, and taking the initial depression level as a current depression level, wherein the relevant information of the patient to be diagnosed at least comprises personal information (such as age, sex, character characteristics, disease history and the like) of the patient to be diagnosed and personal condition introduction information of the patient to be diagnosed, wherein the personal condition introduction information of the patient to be diagnosed can be expressed by the site of the patient to be diagnosed, and the information acquisition component acquires voice information, image information, brain wave information and sitting posture information in the process of expressing the personal condition introduction information of the patient to be diagnosed;
repeatedly executing the multimedia inquiry assembly to inquire the patient to be diagnosed based on the test question cluster corresponding to the current depression level, updating the current depression level based on the feedback information of the test question cluster corresponding to the current depression level and the test question association map of the patient to be diagnosed, which are acquired by the information acquisition assembly, and inquiring the patient to be diagnosed based on the updated test question cluster corresponding to the current depression level until the preset condition is met.
Specifically, the multimedia inquiry module can extract language expression features, brain wave features and sitting posture change features of a patient from voice information, image information, brain wave information and sitting posture information in the process of expressing personal illness state introduction information of the patient to be diagnosed through the feature extraction model, and determine an initial depression level corresponding to the patient to be diagnosed based on the personal information, the language expression features, the brain wave features and the sitting posture change features of the patient to be diagnosed based on the level determination model. The feature extraction model and the hierarchy determination model may be machine learning models such as an artificial neural network (Artificial Neural Network, ANN) model, a recurrent neural network (Recurrent Neural Networks, RNN) model, a Long Short-Term Memory (LSTM) model, and a bi-directional recurrent neural network (BRNN) model.
In some embodiments, the multimedia inquiry module updates the current depression level based on the feedback information of the test question cluster and the test question association map, which are acquired by the information acquisition module and correspond to the current depression level, of the patient to be diagnosed, and the multimedia inquiry module includes:
determining the feedback emotion type of the patient to be diagnosed on the test question cluster corresponding to the current depression level based on the feedback information of the patient to be diagnosed on the test question cluster corresponding to the current depression level, which is acquired by the information acquisition component;
based on feedback emotion type and test question association map of the patient to be diagnosed on the test question cluster corresponding to the current depression level, updating the current depression level.
Fig. 4 is a flowchart illustrating determining a feedback emotion type according to an embodiment of the present application, as shown in fig. 4, in some embodiments, the multimedia inquiry module determines, based on feedback information of a test question cluster corresponding to a current depression level of a patient to be diagnosed obtained by the information collecting module, a feedback emotion type of the patient to be diagnosed for the test question cluster corresponding to the current depression level, including:
extracting voice feedback information of a patient to be diagnosed on a test question cluster corresponding to a current depression level, and determining an initial feedback emotion type of the patient to be diagnosed on the test question cluster corresponding to the current depression level;
determining the first emotion authenticity of the initial feedback emotion type based on voice feedback information of a patient to be diagnosed on a test question cluster corresponding to the current depression level;
determining second emotion authenticity of the initial feedback emotion type based on image feedback information of a patient to be diagnosed on a test question cluster corresponding to the current depression level;
determining third emotion authenticity of the initial feedback emotion type based on brain wave feedback information of a patient to be diagnosed on a test question cluster corresponding to the current depression level;
determining fourth emotion authenticity of the initial feedback emotion type based on pose feedback information of a patient to be diagnosed on a test question cluster corresponding to the current depression level;
the authenticity of the initial feedback emotion type is determined based on the first emotional authenticity, the second emotional authenticity, the third emotional authenticity, and the fourth emotional authenticity.
Specifically, the multimedia inquiry module can generate a sample voice feature, a sample expression feature, a sample brain wave feature and a sample pose change feature corresponding to an initial feedback emotion type through a feature generation model based on language expression features, brain wave features, sitting posture change features and the initial feedback emotion type of a patient extracted from voice information, image information, brain wave information and sitting posture information in the process of expressing personal illness introduction information of the patient to be diagnosed, wherein the feature generation model can generate an impedance network (Generative Adversarial Nets, GAN) model.
The multimedia inquiry module can extract current voice characteristics through the characteristic extraction model based on voice feedback information of a to-be-diagnosed patient on a test question cluster corresponding to a current depression level, and determine first emotion authenticity based on similarity between the current voice characteristics and sample voice characteristics. And extracting current expression features based on image feedback information of the to-be-diagnosed patient on the test question cluster corresponding to the current depression level through a feature extraction model, and determining second emotion authenticity based on similarity between the current expression features and sample expression features. And extracting current brain wave characteristics based on brain wave feedback information of the patient to be diagnosed on the test question cluster corresponding to the current depression level through the characteristic extraction model, and determining third emotion authenticity based on similarity between the current brain wave characteristics and sample brain wave characteristics. And extracting current pose features based on pose feedback information of the patient to be diagnosed on a test question cluster corresponding to the current depression level through a feature extraction model, and determining fourth emotion authenticity based on similarity between the current pose features and sample pose features.
In some embodiments, the authenticity of the initial feedback emotion type may be determined based on the first emotional authenticity, the second emotional authenticity, the third emotional authenticity, and the fourth emotional authenticity according to the following formula:
wherein,for the authenticity of the initial feedback emotion type +.>、/>、/>Is->First emotional authenticity, second emotional authenticity, third emotional authenticity and fourth emotional authenticity, respectively,>、/>、/>is->All are preset weights.
In some embodiments, the repeatedly executing the multimedia inquiry module performs inquiry on the patient to be diagnosed based on the test question cluster corresponding to the current depression level, updates the current depression level based on the feedback information of the test question cluster corresponding to the current depression level and the test question association map of the patient to be diagnosed, which are acquired by the information acquisition module, and performs inquiry on the patient to be diagnosed based on the test question cluster corresponding to the updated current depression level until a preset condition is met, and may include the following procedures:
s1, randomly determining a current sub-cluster from test question clusters corresponding to a current depression level based on a test question association map, extracting a psychological test question from the current sub-cluster as a current psychological test question, performing inquiry on a patient to be diagnosed, and executing S2;
s2, obtaining feedback information of a patient to be diagnosed on a current psychological test question, determining an initial feedback emotion type of the patient to be diagnosed on the current psychological test question and the authenticity of the initial feedback emotion type, executing S3 if the authenticity of the initial feedback emotion type is smaller than or equal to a preset authenticity threshold, and executing S4 if the authenticity of the initial feedback emotion type is larger than the preset authenticity threshold;
s3, extracting an unextracted psychological test question from the current sub-cluster as a current psychological test question, carrying out inquiry on a patient to be diagnosed, and executing S2;
s4, judging whether to update the current depression level according to the initial feedback emotion type, if yes, executing S5 after updating the current depression level, if not, executing S6, for example, if the initial feedback emotion type is negative, updating the current depression level to be the previous depression level, wherein the depression level represented by the depression level before updating is more serious, if the initial feedback emotion type is positive, updating the current depression level to be the next depression level, and if the depression level of the next level is lighter than the depression level represented by the depression level before updating, if the initial feedback emotion type is neutral, executing S5;
s5, judging whether each sub cluster in the test question clusters corresponding to the current depression level is extracted with test questions, wherein the authenticity of the initial feedback emotion type corresponding to the extracted test questions is larger than a preset authenticity threshold value, if not, executing S6, and if so, executing S7;
s6, taking a sub-cluster which is not extracted from the test question clusters corresponding to the current depression level or a sub-cluster which is extracted from the test question and corresponds to the initial feedback emotion type, of which the authenticity is smaller than or equal to a preset authenticity threshold value, as the current sub-cluster, and executing S3;
s7, ending the inquiry.
The disease diagnosis module can be used for generating auxiliary diagnosis results of the patient to be diagnosed based on feedback information of the patient to be diagnosed.
Fig. 5 is a flowchart of generating an auxiliary diagnosis result of a patient to be diagnosed, which is shown in an embodiment of the present application, and as shown in fig. 5, in some embodiments, the disease diagnosis module generates the auxiliary diagnosis result of the patient to be diagnosed based on feedback information of the patient to be diagnosed, including:
establishing a plurality of sample patient diagnostic portraits based on the relevant information of the plurality of sample patients and the plurality of sample inquiry results;
based on the related information of the patient to be diagnosed and the feedback information of the patient to be diagnosed, establishing a patient portrait corresponding to the patient to be diagnosed;
determining a target sample patient diagnostic portrait from a plurality of sample patient diagnostic portraits based on the patient portraits corresponding to the patient to be diagnosed, and generating an auxiliary diagnostic result of the patient to be diagnosed based on the target sample patient diagnostic portraits.
Specifically, the diagnosis similarity between any two sample patients may be calculated based on the related information (for example, personal information, diagnosis information and treatment plan information of the sample patients) and the multiple sample inquiry results of the multiple sample patients, the multiple sample patients are clustered based on the diagnosis similarity between any two sample patients, multiple sample patient diagnosis clusters are generated, for each sample patient diagnosis cluster, a corresponding sample patient diagnosis portrait is established, the sample patient diagnosis portrait with the highest portrait similarity is used as a target sample patient diagnosis portrait based on the portrait similarity between the patient portrait corresponding to the patient to be diagnosed and the multiple sample patient diagnosis portrait, and the auxiliary diagnosis result of the patient to be diagnosed is generated based on the auxiliary diagnosis result corresponding to the target sample patient diagnosis portrait in combination with the psychological test questions and the corresponding answers of the patient to be diagnosed.
The auxiliary treatment module can be used for generating an auxiliary treatment scheme based on the diagnosis result of the patient to be diagnosed.
In some embodiments, the adjuvant therapy module may generate a adjuvant therapy regimen for the patient to be treated based on the target sample patient diagnostic representation, wherein the adjuvant therapy regimen includes at least a psychological grooming adjuvant therapy regimen, a pharmaceutical adjuvant therapy regimen, and/or a transcranial adjuvant therapy regimen.
Fig. 6 is a flowchart of a data processing-based depression assisting diagnosis and treatment method according to an embodiment of the present application, and as shown in fig. 6, a data processing-based depression assisting diagnosis and treatment method may include the following steps:
step 610, acquiring voice inquiry records of a plurality of historical patients, and generating an inquiry question bank;
step 620, performing inquiry on a plurality of sample patients based on the inquiry question bank to obtain a plurality of sample inquiry results;
step 630, processing a plurality of sample inquiry results and establishing a test question association map;
step 640, using the multimedia inquiry module to inquire the patient to be diagnosed based on the inquiry question library and the test question association map;
step 650, acquiring feedback information of the patient to be diagnosed by using the information acquisition component in the process of performing the diagnosis on the patient to be diagnosed by using the multimedia diagnosis component;
step 660, generating an auxiliary diagnosis result of the patient to be diagnosed based on the feedback information of the patient to be diagnosed;
step 670, generating an auxiliary treatment scheme based on the diagnosis result of the patient to be diagnosed.
In some embodiments, a data processing-based depression assisted diagnosis and treatment method may be performed by a data processing-based depression assisted diagnosis and treatment system, and further description of a data processing-based depression assisted diagnosis and treatment method may be referred to in fig. 1 and the related description thereof, which are not repeated here.
Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.
Claims (7)
1. A data processing-based depression assisted diagnosis and treatment system, comprising:
the system comprises a question bank establishing module, a question bank processing module and a question bank processing module, wherein the question bank establishing module is used for acquiring voice question records of a plurality of historical patients and generating a question bank, and the question bank comprises a plurality of psychological test questions;
the sample acquisition module is used for carrying out inquiry on a plurality of sample patients based on the inquiry question library to acquire a plurality of sample inquiry results;
the data processing module is used for processing the plurality of sample inquiry results and establishing a test question association map;
the disease inquiry module comprises a multimedia inquiry module and an information acquisition module, wherein the multimedia inquiry module is used for inquiring a patient to be diagnosed based on the inquiry question bank and the test question association map, and the information acquisition module is used for acquiring feedback information of the patient to be diagnosed in the process of inquiring the patient to be diagnosed by the multimedia inquiry module;
the disease diagnosis module is used for generating an auxiliary diagnosis result of the patient to be diagnosed based on the feedback information of the patient to be diagnosed;
the auxiliary treatment module is used for generating an auxiliary treatment scheme based on the diagnosis result of the patient to be diagnosed;
the question bank establishing module obtains voice question records of a plurality of historical patients and generates a question bank, and the method comprises the following steps:
acquiring voice inquiry records of a plurality of historical patients, determining the occurrence frequency of a plurality of keywords, and determining a plurality of target keywords;
generating a plurality of first psychological inquiry test questions based on the plurality of target keywords and the voice inquiry records of the plurality of historical patients, wherein the plurality of psychological test questions comprise the plurality of first psychological inquiry test questions;
generating a plurality of second psychological diagnosis test questions based on the plurality of first psychological diagnosis test questions through a test question generation model, wherein the plurality of psychological test questions comprise the plurality of second psychological diagnosis test questions;
the data processing module processes the plurality of sample inquiry results and establishes a test question association map, and the data processing module comprises:
clustering the plurality of sample patients based on the disease information of the sample patients to generate a plurality of sample patient clusters;
for each sample patient cluster, classifying the multiple psychological test questions based on a sample inquiry result of each sample patient included in the sample patient cluster, determining multiple test question clusters, and establishing a test question association map, wherein one test question cluster corresponds to one depression level;
the information acquisition component at least comprises voice acquisition equipment, image acquisition equipment, brain wave acquisition equipment and sitting posture acquisition equipment, wherein the voice acquisition equipment is used for acquiring voice feedback information of a test question cluster corresponding to a current depression level of a patient to be diagnosed, the image acquisition equipment is used for acquiring image feedback information of the test question cluster corresponding to the current depression level of the patient to be diagnosed, the brain wave acquisition equipment is used for acquiring brain wave feedback information of the test question cluster corresponding to the current depression level of the patient to be diagnosed, and the sitting posture acquisition equipment is used for acquiring pose feedback information of the test question cluster corresponding to the current depression level of the patient to be diagnosed;
the sitting posture collection device comprises a detection chair, and a cushion pressure sensor, a chair back pressure sensor and an armrest pressure sensor which are arranged on the detection chair.
2. The data processing-based depression assisted diagnosis and treatment system according to claim 1, wherein the multimedia inquiry module performs inquiry on a patient to be diagnosed based on the inquiry question bank and the test question association map, and the system comprises:
acquiring relevant information of the patient to be diagnosed, determining an initial depression level corresponding to the patient to be diagnosed, and taking the initial depression level as a current depression level;
repeatedly executing the multimedia inquiry assembly to inquire the patient to be diagnosed based on the test question cluster corresponding to the current depression level, updating the current depression level based on the feedback information of the test question cluster corresponding to the current depression level and the test question association map of the patient to be diagnosed, which are acquired by the information acquisition assembly, and inquiring the patient to be diagnosed based on the updated test question cluster corresponding to the current depression level until the preset condition is met.
3. The depressive disorder assisted diagnosis and treatment system according to claim 2, wherein the updating the current depressive disorder level by the multimedia inquiry module based on the feedback information of the to-be-diagnosed patient on the test question cluster corresponding to the current depressive disorder level and the test question association map acquired by the information acquisition module comprises:
determining the feedback emotion type of the patient to be diagnosed on the test question cluster corresponding to the current depression level based on the feedback information of the patient to be diagnosed on the test question cluster corresponding to the current depression level, which is acquired by the information acquisition component;
updating the current depression level based on the feedback emotion type of the patient to be diagnosed on the test question cluster corresponding to the current depression level and the test question association map.
4. The depressive disorder assisted diagnosis and treatment system according to claim 1, wherein the multimedia inquiry module determines a feedback emotion type of the patient to be diagnosed for the test question cluster corresponding to the current depression level based on the feedback information of the patient to be diagnosed for the test question cluster corresponding to the current depression level acquired by the information acquisition module, comprising:
extracting voice feedback information of the patient to be diagnosed on the test question cluster corresponding to the current depression level, and determining an initial feedback emotion type of the patient to be diagnosed on the test question cluster corresponding to the current depression level;
determining the first emotion authenticity of the initial feedback emotion type based on the voice feedback information of the patient to be diagnosed on the test question cluster corresponding to the current depression level;
determining second emotion authenticity of the initial feedback emotion type based on image feedback information of the patient to be diagnosed on the test question cluster corresponding to the current depression level;
determining third emotion authenticity of the initial feedback emotion type based on brain wave feedback information of the patient to be diagnosed on the test question cluster corresponding to the current depression level;
determining fourth emotion authenticity of the initial feedback emotion type based on pose feedback information of the patient to be diagnosed on the test question cluster corresponding to the current depression level;
determining the authenticity of the initial feedback emotion type based on the first emotional authenticity, the second emotional authenticity, the third emotional authenticity and the fourth emotional authenticity.
5. A data processing-based depression assisted diagnosis and treatment system according to any one of claims 1-3, wherein said condition diagnosis module generates an assisted diagnosis result of said patient to be diagnosed based on feedback information of said patient to be diagnosed, comprising:
establishing a plurality of sample patient diagnostic portraits based on the relevant information of the plurality of sample patients and the plurality of sample inquiry results;
based on the related information of the patient to be diagnosed and the feedback information of the patient to be diagnosed, establishing a patient portrait corresponding to the patient to be diagnosed;
and determining a target sample patient diagnostic portrait from the plurality of sample patient diagnostic portraits based on the patient portraits corresponding to the patient to be diagnosed, and generating an auxiliary diagnostic result of the patient to be diagnosed based on the target sample patient diagnostic portrait.
6. The data processing-based depression assisted diagnosis and treatment system according to claim 5, wherein the assisted treatment module generates an assisted treatment scheme based on the diagnosis result of the patient to be diagnosed, comprising:
generating an auxiliary treatment scheme of the patient to be diagnosed based on the target sample patient diagnostic portrait, wherein the auxiliary treatment scheme at least comprises a psychological dredging auxiliary treatment scheme, a medicine auxiliary treatment scheme and/or a transcranial auxiliary treatment scheme.
7. A data processing-based depression assisted diagnosis and treatment method, which is based on the system according to any one of claims 1 to 6, characterized by comprising:
acquiring voice inquiry records of a plurality of historical patients, and generating an inquiry question library, wherein the inquiry question library comprises a plurality of psychological test questions;
based on the inquiry question library, carrying out inquiry on a plurality of sample patients to obtain a plurality of sample inquiry results;
processing the multiple sample inquiry results and establishing a test question association map;
using a multimedia inquiry assembly to inquire a patient to be diagnosed based on the inquiry question library and the test question association map;
acquiring feedback information of the patient to be diagnosed by using an information acquisition component in the process of inquiring the patient to be diagnosed by using the multimedia inquiry component;
generating an auxiliary diagnosis result of the patient to be diagnosed based on the feedback information of the patient to be diagnosed;
and generating an auxiliary treatment scheme based on the diagnosis result of the patient to be diagnosed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311675914.3A CN117373658B (en) | 2023-12-08 | 2023-12-08 | Data processing-based auxiliary diagnosis and treatment system and method for depression |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311675914.3A CN117373658B (en) | 2023-12-08 | 2023-12-08 | Data processing-based auxiliary diagnosis and treatment system and method for depression |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117373658A CN117373658A (en) | 2024-01-09 |
| CN117373658B true CN117373658B (en) | 2024-03-08 |
Family
ID=89404521
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311675914.3A Active CN117373658B (en) | 2023-12-08 | 2023-12-08 | Data processing-based auxiliary diagnosis and treatment system and method for depression |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117373658B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20190066689A (en) * | 2017-12-06 | 2019-06-14 | 고려대학교 산학협력단 | Medical service assisted system and method thereof |
| RU2711976C1 (en) * | 2018-11-08 | 2020-01-23 | Инна Юрьевна Жовнерчук | Method for remote recognition and correction using a virtual reality of a psychoemotional state of a human |
| CN115905187A (en) * | 2023-02-16 | 2023-04-04 | 江苏一道云科技发展有限公司 | Intelligent proposition system for cloud computing engineering technician authentication |
| CN116153515A (en) * | 2023-03-07 | 2023-05-23 | 山东第一医科大学附属省立医院(山东省立医院) | Cardiovascular patient psychological condition evaluation method and system based on inquiry information |
| CN116910172A (en) * | 2023-07-17 | 2023-10-20 | 杭州卓深科技有限公司 | Follow-up table generation method and system based on artificial intelligence |
| CN117079798A (en) * | 2023-07-05 | 2023-11-17 | 上海深至信息科技有限公司 | Intelligent pre-consultation and rapid diagnosis and treatment system and method based on large language model |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10874340B2 (en) * | 2014-07-24 | 2020-12-29 | Sackett Solutions & Innovations, LLC | Real time biometric recording, information analytics and monitoring systems for behavioral health management |
| JP2021529382A (en) * | 2018-06-19 | 2021-10-28 | エリプシス・ヘルス・インコーポレイテッド | Systems and methods for mental health assessment |
-
2023
- 2023-12-08 CN CN202311675914.3A patent/CN117373658B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20190066689A (en) * | 2017-12-06 | 2019-06-14 | 고려대학교 산학협력단 | Medical service assisted system and method thereof |
| RU2711976C1 (en) * | 2018-11-08 | 2020-01-23 | Инна Юрьевна Жовнерчук | Method for remote recognition and correction using a virtual reality of a psychoemotional state of a human |
| CN115905187A (en) * | 2023-02-16 | 2023-04-04 | 江苏一道云科技发展有限公司 | Intelligent proposition system for cloud computing engineering technician authentication |
| CN116153515A (en) * | 2023-03-07 | 2023-05-23 | 山东第一医科大学附属省立医院(山东省立医院) | Cardiovascular patient psychological condition evaluation method and system based on inquiry information |
| CN117079798A (en) * | 2023-07-05 | 2023-11-17 | 上海深至信息科技有限公司 | Intelligent pre-consultation and rapid diagnosis and treatment system and method based on large language model |
| CN116910172A (en) * | 2023-07-17 | 2023-10-20 | 杭州卓深科技有限公司 | Follow-up table generation method and system based on artificial intelligence |
Non-Patent Citations (3)
| Title |
|---|
| 基于语音的抑郁症识别;潘玮;汪静莹;刘天俐;刘晓倩;刘明明;胡斌;朱廷劭;;科学通报;20180720(第20期);全文 * |
| 美国教育评价研究的知识基础与热点――基于2000―2014年8种SSCI期刊的可视化分析;邱均平;欧玉芳;;中国地质大学学报(社会科学版);20160430(第02期);全文 * |
| 计算机辅助认知行为治疗对抑郁症的疗效;成云，崔界峰;中国健康心理学杂志;20220731;第30卷(第7期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117373658A (en) | 2024-01-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110516161B (en) | Recommendation method and device | |
| US20220148191A1 (en) | Image segmentation method and apparatus and storage medium | |
| Kwasigroch et al. | Deep CNN based decision support system for detection and assessing the stage of diabetic retinopathy | |
| CN106709254B (en) | A kind of medical diagnosis robot system | |
| CN109272048B (en) | Pattern recognition method based on deep convolutional neural network | |
| US10854335B2 (en) | Computer aided medical method and medical system for medical prediction | |
| WO2019024380A1 (en) | Intelligent traditional chinese medicine diagnosis method, system and traditional chinese medicine system | |
| JP2020149711A5 (en) | ||
| WO2018236565A1 (en) | Dynamic self-learning medical image method and system | |
| WO2020103683A1 (en) | Method and system for individualized prediction of mental illness on basis of brain function map monkey-human cross-species migration | |
| KR20170061222A (en) | The method for prediction health data value through generation of health data pattern and the apparatus thereof | |
| WO2019242155A1 (en) | Voice recognition-based health management method and apparatus, and computer device | |
| CN114842977B (en) | Medical decision-making system based on medical big data and artificial intelligence | |
| CN108647229B (en) | Virtual person model construction method based on artificial intelligence | |
| Dhar | An adaptive intelligent diagnostic system to predict early stage of parkinson's disease using two-stage dimension reduction with genetically optimized lightgbm algorithm | |
| CN116705300A (en) | Medical decision assistance method, system and storage medium based on sign data analysis | |
| Reddy et al. | Prediction of heart disease using decision tree approach | |
| KR20110098286A (en) | Self health diagnosis system of oriental medicine using fuzzy inference method | |
| Anusri et al. | An early prediction of Parkinson’s disease using facial emotional recognition | |
| CN117373658B (en) | Data processing-based auxiliary diagnosis and treatment system and method for depression | |
| CN114120035A (en) | Medical image recognition training method | |
| Meshram et al. | MCBM: Implementation Of Multiclass And Transfer Learning Algorithm Based On Deep Learning Model For Early Detection Of Diabetic Retinopathy | |
| Khorasani et al. | Agricultural Harvester Sound Classification using Convolutional Neural Networks and Spectrograms | |
| Mirarchi et al. | Data mining techniques for vestibular data classification | |
| Priya et al. | A novel intelligent diagnosis and disease prediction algorithm in green cloud using machine learning approach |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |