WO2023198065A1 - Physiological data collecting apparatus and method, physiological data processing apparatus and method and physiological data presenting apparatus and method for handling crisis and non-crisis situations - Google Patents

Physiological data collecting apparatus and method, physiological data processing apparatus and method and physiological data presenting apparatus and method for handling crisis and non-crisis situations Download PDF

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WO2023198065A1
WO2023198065A1 PCT/CN2023/087635 CN2023087635W WO2023198065A1 WO 2023198065 A1 WO2023198065 A1 WO 2023198065A1 CN 2023087635 W CN2023087635 W CN 2023087635W WO 2023198065 A1 WO2023198065 A1 WO 2023198065A1
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crisis
physiological data
intervention
classification
module
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PCT/CN2023/087635
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French (fr)
Chinese (zh)
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孟令忠
廖可
王炜
张昕
赵舒展
宋伟
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北京优理医疗器械有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/906—Clustering; Classification
    • 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
    • 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
    • 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
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • 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
    • G16H70/00—ICT specially adapted for the handling or processing of medical references
    • G16H70/20—ICT specially adapted for the handling or processing of medical references relating to practices or guidelines

Definitions

  • the invention belongs to the field of physiological data processing, and specifically relates to a physiological data collection, processing and presentation device and method for processing crisis and non-crisis situations.
  • perioperative management methods based on optimizing physiological indicators have been widely used in perioperative management.
  • Li et al. (BMJ Open 2019;9:e025337) observed the AUC index of physiological data such as blood pressure and heartbeat during spinal surgery and found that the optimized physiological index interval is directly related to the prognosis of surgery.
  • Helmerhorst et al. (Care Med 2017; 45:187-195) observed that the hyperoxia index when the PaO2 data exceeds the upper limit is correlated with the outcome data in severe cases.
  • PCT/CN2021/108718 used muscle tissue oxygen (SmtO2) selected from the abdominal muscles and/or forearms, combined with hemodynamic signs including cardiac output (CO), blood pressure (BP), etc., perform perioperative management by calculating the AUC index and duration, thereby reducing the prevalence of postoperative nausea and vomiting (PONV) in patients.
  • Meng et al. PCT/CN2021/108716) analyzed hemodynamic data including cardiac output (CO), blood pressure (BP), stroke rate (SV), heart rate (HR), and systemic vascular resistance (SVR).
  • stroke volume variation (SVV) is compared with the baseline value and the upper and lower ranges, so as to provide high-quality perioperative management of surgical patients.
  • users need to obtain a variety of high-quality physiological data from multiple monitoring instruments, and obtain optimized physiological indicators through calculation, so as to conduct high-quality perioperative management.
  • perioperative data management methods based on physiological indicators have been widely used.
  • Lee & Jung (Nature SCieNtifiC REPORTS (2016) 8:1527) describe a software Vital Recorder that connects multiple anesthesia monitoring instruments to automatically record high-resolution, time-synchronized physiological data.
  • this system generally stores and displays all physiological data. It is only suitable for use after surgery and when doctors conduct scientific research, and cannot meet the needs of perioperative management.
  • Johnson et al. Mimic-iii, a freely accessible critical care database.Sci.data 3(2016).) collected a large amount of patient physiological data through monitors in two hospitals and constructed a public data set Medical Information Mart for Intensive Care( MIMIC-III).
  • MIMIC-III Medical Information Mart for Intensive Care
  • doctors can obtain massive, high-frequency physiological data from a variety of monitoring instruments.
  • the relevant data are not properly classified and displayed to assist in attribution analysis for better perioperative data management.
  • Mindray discloses a monitoring method that classifies physiological sign parameters according to their correlation to obtain a set of physiological signs, and displays the set of physiological signs in a slice window.
  • data classification display in the slice window is still not intuitive and lacks the data stratification required for clinical practice.
  • Chen Yongming (publication number CN 112331325 A) disclosed a basic life support decision-making system under artificial intelligence. It includes modules for monitoring the circulatory system, respiratory system, body temperature, anesthesia depth, muscle relaxation and other modules. Based on the monitoring data, the decision-making system automatically identifies abnormalities in vital signs, makes cause analysis and judgment, and generates decision-making plans. Monitoring indicators are classified, and the decision-making system classifies abnormal indicators separately, and automatically generates medical decisions based on physical signs and treatment guidelines. However, only classifying normal values and abnormal values after classification cannot perform effective attribution analysis.
  • a perioperative risk assessment and clinical intelligent decision-making assistance system which performs structured perioperative risk assessment and prediction, and Intelligent assistance for perioperative decision-making. Based on data from various instruments and electronic medical record systems, we can judge and predict risk events and provide corresponding intervention technical solutions. However, there is no multi-level classification judgment for risk events, and there is no corresponding automated processing and intervention process.
  • the alarm system of existing monitoring instruments will give an alarm prompt when a certain monitoring parameter exceeds the threshold range, but it is limited to independent parameter judgment and does not provide a corresponding processing process.
  • the present invention provides a physiological data collection, processing, and presentation method, which includes the following steps:
  • step (3) Based on the classification results obtained in step (2), automatically analyze and present the intervention auxiliary plan for intervention;
  • the method of obtaining physiological data can use methods known to those skilled in the art, such as connecting physiological data detection instruments or monitoring instruments through wired or wireless (such as cloud) means, where The detection instruments or monitoring instruments include but are not limited to perioperative management and monitoring instruments to obtain physiological data.
  • the physiological data is physiological index data of a living human body or animal body.
  • the classification judgment of crisis and non-crisis is that if no timely intervention is performed, irreversible damage or even death will be caused to the subject.
  • the processing and analysis of the physiological data include: obtaining the original physiological data retained after screening through quality analysis and screening, that is, the preferred physiological data; optionally analyzing the preferred physiological data.
  • the data is optimized and analyzed to obtain optimized physiological data.
  • the physiological data is processed and analyzed in the physiological data quality assessment module, the obtained optimal physiological data is sent to the crisis/non-crisis classification module for classification, and the filtered out repeated physiological data is sent to Physiological data transmission module.
  • the physiological data processing and analysis can also use the physiological data collection and management device and processing method described in Chinese Patent No. 202111168482.8.
  • the quality analysis and screening can be performed in the physiological data quality analysis module.
  • the optimization analysis can be performed in the physiological data optimization analysis module.
  • the crisis/non-crisis classification can be achieved by the following two methods:
  • Method 1 Process the physiological data with no outlier data and qualified collection quality to obtain the analysis of the current physiological data. If it exceeds the threshold, mark the abnormality and combine multiple abnormal flags to provide a crisis or non-crisis status judgment and two level judgment;
  • the processing of physiological data includes: AUC analysis of a single physiological parameter, and correlation analysis between multiple physiological parameters;
  • Method 2 Import the physiological data with no outlier data and qualified collection quality into the analysis model, and the analysis model will give the crisis or non-crisis state judgment and secondary judgment.
  • the analysis model includes a crisis/non-crisis classification model and a two-level classification model.
  • the crisis/non-crisis classification model is based on a normalized model, a text processing model, and a timing analysis model.
  • the normalized model provides basic information data
  • the text processing model provides Historical data
  • the time series analysis model provides optimized physiological data.
  • the normalized model is constructed based on basic information of the subject providing physiological data.
  • the basic information includes the subject's date of birth, height, weight, gender, etc.
  • the text processing model is built based on the base case of the object providing physiological data.
  • the basic information includes disease diagnosis, medical history, chronic medical history, drug history, PONV, type of surgery, anesthetic drugs, ASA level, etc.
  • the time series analysis model is constructed based on physiological data with outliers removed and a time series abnormal data (abnormal) analysis model.
  • time series abnormal data (abnormal) analysis model includes correction judgment: abnormal data of a single parameter and/or a single device is directly judged as abnormal data; abnormal data of multiple parameters and multiple devices is sent to crisis/non-crisis classification. crisis/non-crisis judgments are made in the model.
  • the two-level classification model is based on a normalized model and a crisis/non-crisis classification model.
  • the crisis classification includes but is not limited to the following classifications based on circulatory, respiratory, neurological, and other dangerous physiological indicator data: circulatory crisis classification, respiratory crisis classification, neurological crisis classification, and other crisis classifications. .
  • the circulatory crisis classification includes: cardiac arrest, bradycardia, supraventricular tachycardia, ventricular fibrillation/ventricular tachycardia, massive hemorrhage, severe hypotension, severe hypertension, myocardial ischemia, right heart failure, etc.;
  • the respiratory crisis classification includes: bronchospasm, pulmonary embolism, abnormally increased airway pressure, severe hypoxemia, tension pneumothorax, etc.;
  • the neurological crisis classification includes: delayed awakening, high spinal anesthesia, etc.;
  • the other crisis categories include: trauma rescue, allergic reaction, blood transfusion reaction, local anesthetic poisoning, malignant hyperthermia, feeding failure, sudden power outage, etc.
  • the non-crisis classification is based on non-risk physiology of circulation, breathing, brain, temperature, etc.
  • the indicator data includes but is not limited to the following classifications: circulation non-crisis classification, respiratory non-crisis classification, brain non-crisis classification and temperature non-crisis classification;
  • the circulatory non-crisis classification includes but is not limited to: slow/fast heart rate, low/high blood pressure, low volume, low/high cardiac output, low/high tissue oxygen;
  • the respiratory non-crisis classification includes but is not limited to: high tidal volume, high driving pressure, high plateau pressure, high peak airway pressure, low/high oxygenation, hyperventilation, and hypercapnia. ;
  • the brain non-crisis classification includes but is not limited to: low/high cerebral oxygen, deep/shallow anesthesia, high intracranial pressure, poor cerebral relaxation, and low cerebral perfusion pressure;
  • the temperature non-crisis includes but is not limited to: low/high body temperature.
  • the intervention auxiliary plan includes: accessing auxiliary treatment equipment and/or other intervention equipment.
  • the auxiliary treatment equipment includes but is not limited to:
  • Medication device used for medication adjustment, that connects a syringe pump to the subject.
  • step (3) the automatic analysis is performed in the cause and intervention analysis module, which contains corresponding causes and optional intervention measures for various crisis/non-crisis classifications and secondary classifications. .
  • step (3) when the intervention auxiliary plan presented is not optimal, it can be adjusted manually.
  • the intervention in step (3), may be manual intervention or automatic machine intervention.
  • the automatic machine intervention is preferably machine operation intervention after manual confirmation.
  • step (3) the intervention assistance plan is presented to the user through the display device.
  • the present invention also provides a display method, which includes the steps of presenting crisis/non-crisis classification results, secondary classification results, crisis/non-crisis status, intervention assistance plans and/or post-intervention effects to the user through a display device.
  • the present invention also provides the above-mentioned physiological data collection, processing, and presentation device to implement the above-mentioned physiological data collection, processing, and presentation methods and/or display methods.
  • the device includes a physiological data transmission module, a crisis/non-crisis classification module, a secondary classification module, a cause and intervention analysis module, an intervention module and an intervention effect analysis module.
  • the physiological data transmission module may include an element connected to a detection instrument or monitoring instrument that provides physiological data, thereby transmitting the data collected or stored by the detection instrument or monitoring instrument to the physiological data collection and processing ,present device to obtain physiological data.
  • the physiological data transmission module may be a wired or wireless (such as cloud) transmission module known in the art.
  • the crisis/non-crisis classification module is used to classify the preferred physiological data or raw physiological data into crisis and non-crisis.
  • the device further includes a physiological data quality assessment module, which is respectively connected to the physiological data transmission module and the crisis/non-crisis classification module.
  • the optimal physiological data obtained by the physiological data quality assessment module is sent to the crisis/non-crisis classification module for classification, and the filtered out repeated physiological data is sent to the physiological data transmission module.
  • the secondary classification module is used for classifying crisis/non-crisis secondary data.
  • the cause and intervention analysis module includes various crisis/non-crisis classifications and secondary Classified corresponding causes and optional intervention measures provide users with auxiliary intervention plans in corresponding situations.
  • the intervention module is connected to the cause and intervention measure analysis module, and performs manual intervention and/or automatic machine intervention according to the intervention auxiliary plan.
  • the intervention effect analysis module is connected to the crisis/non-crisis classification module, the secondary classification module and the intervention module respectively, and is used to analyze the effect after the intervention and determine whether to continue the intervention.
  • the device further includes a display module for displaying crisis/non-crisis classification results, secondary classification results, crisis/non-crisis status, intervention assistance programs and/or post-intervention effects.
  • the device is presented to the user.
  • the device may further include at least one interactive module to allow the user to control the operation of at least one other module, such as opening, closing, pausing or continuing to run other modules; or, the interactive module may allow Users process the classification and intervention of physiological data, for example: manually modify, classify or mark the classification of the above physiological data, select or adjust intervention measures, and/or transmit the data intervention results to other devices.
  • the interactive module can input control instructions through a touch screen or mechanical keys.
  • the device may include additional transmission elements to transmit the classification results, intervention measures, and/or intervention effects to other devices.
  • the above-mentioned modules in the device may also include one or more data storage elements to store classification results, intervention measures, and/or intervention effects according to the needs of each module.
  • the present invention also provides a method for preventing and/or treating diseases, including using the physiological data collection, processing, presenting method or display method to prevent and/or treat diseases.
  • the present invention also provides a management method for perioperative subjects, including using the physiological data collection, processing, presentation method or display method to obtain the physical condition of the subject, and selecting or not selecting to prevent and/or treat diseases according to the physical condition. Methods.
  • the present invention also provides the use of the above physiological data collection, processing and presentation methods, display methods or physiological data collection, processing and presentation devices in physiological data analysis.
  • the present invention also provides the use of the above physiological data collection, processing and presentation methods, physiological data display methods or physiological data collection, processing and presentation devices in preventing and/or treating diseases.
  • the present invention also provides the use of the above physiological data collection, processing and presentation methods, physiological data display methods or physiological data collection, processing and presentation devices in perioperative management of subjects.
  • the present invention also provides the use of the above physiological data collection, processing, and presentation device in manufacturing a system for preventing and/or treating diseases, or manufacturing a system for perioperative management of subjects.
  • the perioperative period includes the period before surgery, during surgery and after surgery.
  • the present invention solves the problem of how to improve efficiency in the face of massive physiological data from a variety of devices. It focuses on the problem and proposes a clear solution route based on data collection, processing and presentation in a targeted and purposeful manner, combined with data processing and analysis. , feedback tracks intervention effects, leading to more effective technical issues in crisis and non-crisis management.
  • intervention steps are given, and automated intervention or auxiliary intervention programs can be provided, thereby reducing the user's manual workload and improving the quality of medical care;
  • Figure 1 is a schematic flowchart of the method for collecting, processing, and presenting physiological data for handling crisis and non-crisis situations
  • Figure 2 is a schematic structural diagram of a device for collecting, processing, and presenting physiological data for handling crisis and non-crisis situations
  • Figure 3 is a schematic structural diagram of the intervention module
  • Figure 4 is a schematic display diagram of object status monitoring
  • Figure 5 is a schematic structural diagram of the analysis model
  • Figure 6 is a schematic diagram showing crisis status classification
  • Figure 7 is a schematic diagram showing the classification of non-crisis states
  • Figure 8 is an example of optimized processing and analysis of physiological index data
  • Figure 9 is another display schematic diagram of object status monitoring
  • Figure 10 is a schematic diagram showing the hemodynamic pyramid
  • Figure 11 is another schematic diagram of status monitoring of an object
  • Figure 12 is another schematic diagram of status monitoring of an object
  • Figure 13 is a schematic diagram of the optimal management of physiological indicator data in a slow heart rate state
  • Figure 14 is a schematic diagram of the optimal management of physiological indicator data under a fast heart rate state
  • Figure 15 is a schematic diagram of optimal management of physiological index data under low blood pressure
  • Figure 16 is a schematic diagram of the optimal management of physiological index data in a state of high blood pressure
  • Figure 17 is a schematic diagram of optimal management of physiological index data in a low capacity state
  • Figure 18 is a schematic diagram of the optimal management of physiological index data when cardiac output is low
  • Figure 19 is a schematic diagram of optimal management of physiological index data in a state of low tissue oxygen
  • Figure 20 is a schematic diagram of the optimal management of physiological index data in a state of high tissue oxygen
  • Figure 21 is a schematic diagram of the optimal management of physiological index data when the tidal volume is too large
  • Figure 22 is a schematic diagram of optimal management of physiological index data when airway pressure is high
  • Figure 23 is a schematic diagram of optimal management of physiological index data in a state of low oxygenation
  • Figure 24 is a schematic diagram of optimal management of physiological index data in a state of high oxygenation
  • Figure 25 is a schematic diagram of optimal management of physiological index data in a hyperventilation state
  • Figure 26 is a schematic diagram of optimal management of physiological index data in a state of hypercapnia
  • Figure 27 is a schematic diagram of optimal management of physiological index data in a state of low cerebral oxygenation
  • Figure 28 is a schematic diagram of the optimal management of physiological index data in a state of high cerebral oxygenation
  • Figure 29 is a schematic diagram of the optimal management of physiological index data under deep anesthesia
  • Figure 30 is a schematic diagram of the optimal management of physiological index data under light anesthesia
  • Figure 31 is a schematic diagram of optimal management of physiological index data in a state of high intracranial pressure
  • Figure 32 is a schematic diagram of the optimal management of physiological index data in a state of poor cerebral relaxation
  • Figure 33 is a schematic diagram of optimal management of physiological index data under low cerebral perfusion pressure
  • Figure 34 is a schematic diagram of optimal management of physiological indicator data in cardiac arrest state
  • Figure 35 is a schematic diagram of optimized management of cardiopulmonary resuscitation physiological indicator data
  • Figure 36 is a schematic diagram of optimal management of physiological index data in H’s state
  • Figure 37 Schematic diagram of optimal management of physiological indicator data for hyperkalemia
  • FIG 38 is a schematic diagram of T’s physiological index data optimization management
  • Figure 39 is a schematic diagram of intervention for bradycardia in a crisis state
  • Figure 40 is a schematic diagram of intervention for supraventricular tachycardia in a crisis state
  • Figure 41 is a schematic diagram of intervention for ventricular fibrillation/ventricular tachycardia in a crisis state
  • Figure 42 is a schematic diagram of the intervention of allergic reactions in a crisis state
  • Figure 43 is a schematic diagram of intervention for bronchospasm in a crisis state
  • Figure 44 is a schematic diagram of intervention for delaying recovery in a crisis state
  • Figure 45 is a schematic diagram of intervention for pulmonary embolism in a crisis state
  • Figure 46 is a schematic diagram of intervention for massive bleeding in a crisis state
  • Figure 47 is a schematic diagram of intervention for abnormally increased airway pressure in a crisis state
  • Figure 48 is a schematic diagram of high spinal anesthesia intervention in a crisis state
  • Figure 49 is a schematic diagram of intervention for severe hypertension in a crisis state
  • Figure 50 is a schematic diagram of intervention for severe hypotension in a crisis state
  • Figure 51 is a schematic diagram of intervention for severe hypoxemia in a crisis state
  • Figure 52 is a schematic diagram of intervention for V/Q mismatch
  • Figure 53 is a schematic diagram of the intervention of local anesthesia in a crisis state
  • Figure 54 is a schematic diagram of intervention for malignant hyperthermia in a crisis state
  • Figure 55 is a schematic diagram of the intervention in Single Forest
  • Figure 56 is a schematic diagram of the intervention of cardiac hypoxia in a crisis state
  • Figure 57 is a schematic diagram of intervention for oxygen supply failure in a crisis state
  • Figure 58 is a schematic diagram of intervention for tension pneumothorax in crisis state
  • Figure 59 is a schematic diagram of intervention in a sudden power outage in a crisis state
  • Figure 60 is a schematic diagram of intervention for right heart failure in a crisis state
  • Figure 61 is a schematic diagram of ultrasound signs of right heart failure
  • Figure 62 is a schematic diagram of management of the right ventricle
  • Figure 63 is a schematic diagram of intervention for transfusion reaction in crisis state
  • Figure 64 is a schematic diagram of trauma rescue intervention in a crisis state
  • Figure 65 is a schematic diagram of the preliminary investigation
  • Figure 66 shows the Glasgow Coma Scale
  • Figure 67 shows the secondary survey map
  • Figure 68 is a schematic diagram of operating room preparation
  • Figure 69 is a schematic diagram of anesthesia induction/airway establishment
  • Figure 70 is a schematic diagram of intervention for traumatic brain injury
  • Figure 71 is a schematic diagram showing the physiological indicators to be optimized in Embodiment 3.
  • Figure 72 is a schematic diagram showing the auxiliary intervention plan for fast heart rate in Embodiment 3.
  • S1 Connect multiple monitoring instruments to obtain the physiological data of the subject; for example, the status monitoring display of the subject is as shown in Figure 4 or Figure 9;
  • the processing and analysis of the physiological data include: obtaining the original physiological data retained after screening through quality analysis and screening, that is, the optimized physiological data; performing optimization analysis on the optimized physiological data to obtain optimized physiological data;
  • the physiological data processing and analysis also adopt the physiological data collection and management device and processing method recorded in Chinese Patent No. 202111168482.8.
  • the optimization of physiological data is shown in Figure 8.
  • step S3 Based on the classification results obtained in step S2, automatically analyze and present the intervention auxiliary plan, manual intervention or automatic intervention;
  • S4 Monitor the changes in physiological parameters in real time, obtain physiological data after intervention, analyze and view the crisis/non-crisis classification and secondary classification after intervention, and consider whether to intervene again according to the situation;
  • the crisis/non-crisis classification is achieved through the following two methods:
  • Method 1 Process the physiological data with no outlier data and qualified collection quality to obtain the analysis of the current physiological data. If it exceeds the threshold, mark the abnormality and combine multiple abnormal flags to provide a crisis or non-crisis status judgment and two level judgment;
  • the processing of physiological data includes: AUC analysis of a single physiological parameter, and correlation analysis between multiple physiological parameters;
  • Method 2 Import the physiological data with no outlier data and qualified collection quality into the analysis model, and the analysis model will give the crisis or non-crisis state judgment and secondary judgment;
  • Analytical models include crisis/non-crisis classification models and two-level classification models
  • the crisis/non-crisis classification model is based on a normalization model, a text processing model, and a time series analysis model.
  • the normalization model provides basic information data
  • the text processing model provides historical data.
  • the time series analysis model provides optimized physiological data.
  • the normalized model is built based on basic information about the subject providing physiological data.
  • the basic information includes the subject's date of birth, height, weight, gender, etc.
  • Text processing models are built on base cases of objects that provide physiological data.
  • the basic information includes disease diagnosis, medical history, chronic medical history, drug history, PONV, type of surgery, anesthetic drugs, ASA level, etc.
  • the time series analysis model is built based on the physiological data and time series abnormal data (abnormal) analysis model that removes outliers.
  • the time series abnormal data (abnormal) analysis model includes correction judgment: abnormal data of a single parameter and/or a single device are directly judged as abnormal data; abnormal data of multiple parameters and multiple devices are sent to the crisis/ Crisis/non-crisis judgments are made in the non-crisis classification model.
  • the two-level classification model is based on the normalized model and the crisis/non-crisis classification model.
  • Figures 10-12 respectively present various physiological indicators, physiological states, etc. that need to be monitored, screened and/or optimized.
  • Figures 13 to 38 illustrate schematic diagrams of optimal management of physiological index parameters in various states, including but not limited to slow heart rate, fast heart rate, low blood pressure, high blood pressure, low capacity, and low cardiac output. etc. status.
  • Figures 39 to 70 illustrate auxiliary intervention operations in various crisis situations, including but not limited to bradycardia, supraventricular tachycardia, ventricular fibrillation/ventricular tachycardia, allergic reactions and other crisis situations.
  • the device includes a physiological data transmission module, a crisis/non-crisis classification module, a secondary classification module, a cause and intervention analysis module, an intervention module and an intervention effect analysis module;
  • the physiological data transmission module includes an element connected to a detection instrument or monitoring instrument that provides physiological data, thereby transmitting the data collected or stored by the detection instrument or monitoring instrument to the physiological data collection, processing, and presentation. device to obtain physiological data.
  • the physiological data transmission module may be a wired or wireless (such as cloud) transmission module known in the art.
  • the crisis/non-crisis classification module is used to classify the preferred physiological data or raw physiological data into crisis and non-crisis.
  • the device further includes a physiological data quality assessment module, which is respectively connected to the physiological data transmission module and the crisis/non-crisis classification module.
  • the optimal physiological data obtained by the physiological data quality assessment module is sent to the crisis/non-crisis classification module for classification, and the filtered out repeated physiological data is sent to the physiological data transmission module.
  • the secondary classification module is used to classify crisis/non-crisis secondary data
  • the cause and intervention measure analysis module contains corresponding causes and optional intervention measures for various crisis/non-crisis classifications and secondary classifications, and provides users with intervention auxiliary plans in corresponding states;
  • the intervention module is connected to the cause and intervention measure analysis module, and performs manual intervention and/or automatic machine intervention according to the intervention auxiliary plan; as shown in Figure 3;
  • the intervention effect analysis module is connected to the crisis/non-crisis classification module, the secondary classification module and the intervention module respectively, and is used to analyze the effect after the intervention and determine whether to continue the intervention;
  • the device further includes a display module for displaying crisis/non-crisis classification results, secondary classification results, crisis/non-crisis status, intervention auxiliary plans and/or post-intervention effects through the display device. presented to the user;
  • the device may further include at least one interactive module to allow the user to control the operation of at least one other module, such as opening, closing, pausing or continuing to run other modules; or, the interactive module may allow the user Process the classification and intervention of physiological data, for example: manually modify, classify or mark the classification of the above physiological data, select or adjust the intervention, and/or transmit the data intervention results to other devices.
  • the interactive module can input control instructions through a touch screen or mechanical keys.
  • the device may include additional transmission elements to transmit the classification results, intervention measures, and/or intervention effects to other devices.
  • the above-mentioned modules in the device further include one or more data storage components to store classification results, intervention measures, and/or intervention effects according to the needs of each module.
  • the above method is performed in the device shown in FIG. 2 .
  • a method for preventing and/or treating diseases including using the physiological data collection, processing, presentation method or display method prevent and/or treat disease.
  • a method for managing perioperative subjects including using the physiological data collection, processing, presentation method or display method to obtain the physical condition of the subject, and selecting or not selecting methods to prevent and/or treat diseases according to the physical condition.
  • the perioperative period includes the period before, during, and after surgery.
  • the model provides crisis or non-crisis state judgments and secondary judgments ("Excellent Reason” corresponds to non-crisis, and "Crisis” corresponds to crisis.
  • the above method solves the problem of how to improve efficiency and be targeted and purposeful in the face of massive physiological data from multiple devices. Based on the collection, processing and presentation of data, we focus on the problem, propose a clear solution route, and combine it with data processing and analysis to provide feedback and track the intervention effect, so as to more effectively manage technical issues of crisis and non-crisis management.

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Abstract

The present invention relates to a physiological data collecting apparatus and method, physiological data processing apparatus and method and physiological data presenting apparatus and method for handling crisis and non-crisis situations. The presenting method comprises the following steps: (1) acquiring physiological data; (2) performing crisis/non-crisis classification and secondary classification by means of processing and analyzing the physiological data; (3) according to a classification result which is obtained in step (2), automatically analyzing and presenting an intervention assistance scheme, and performing intervention; and (4) acquiring intervened physiological data, analyzing and checking intervened crisis/non-crisis classification and secondary classification, and depending on the situation, considering whether to perform intervention again. In the present invention, in the face of massive physiological data from various devices, the technical problems of how to improve the efficiency, how to purposefully focus problems in a targeted manner on the basis of data collection, processing and presentation so as to provide a clear solution route, and how to feed back and track an intervention effect in combination with data processing and analysis so as to perform crisis and non-crisis management more effectively are solved.

Description

一种对危机和非危机情况进行处理的生理数据收集、处理、呈现装置和方法A physiological data collection, processing, and presentation device and method for handling crisis and non-crisis situations
本申请要求申请人于2022年4月11日向中国国家知识产权局提交的专利申请号为202210375337.5，发明名称为“一种对危机和非危机情况进行处理的生理数据收集、处理、呈现装置和方法”的在先申请的优先权。所述在先申请的全文通过引用的方式结合于本申请中。This application requires the applicant to submit a patent application number to the State Intellectual Property Office of China on April 11, 2022, with the patent application number 202210375337.5, and the invention name is "A physiological data collection, processing, and presentation device and method for handling crisis and non-crisis situations" ” the priority of the earlier application. The entirety of said prior application is incorporated into this application by reference.
技术领域Technical field
本发明属于生理数据处理领域，具体涉及一种对危机和非危机情况进行处理的生理数据收集、处理、呈现装置和方法。The invention belongs to the field of physiological data processing, and specifically relates to a physiological data collection, processing and presentation device and method for processing crisis and non-crisis situations.
背景技术Background technique
随着电子技术的发展，用于围术期、ICU、重症、急诊监护的监护仪器越来越多，采集的人体生理数据种类更多、频率更高。用户在围术期、ICU、重症、急诊监护等各类工作中，面对海量的生理数据、众多的监护仪器，无法高效获得有价值的高质量数据。如何从海量的数据中，及时、精准地判断对象的危机/非危机状况，并且对危机/非危机状况作何处理，需要消耗大量的人力资源，还存在处理流程不规范，容易出错。With the development of electronic technology, more and more monitoring instruments are used for perioperative, ICU, intensive care, and emergency care, and more types of human physiological data are collected at a higher frequency. In various tasks such as perioperative, ICU, critical care, and emergency care, users are faced with massive physiological data and numerous monitoring instruments, and cannot efficiently obtain valuable and high-quality data. How to timely and accurately determine the crisis/non-crisis status of an object from massive amounts of data, and how to deal with the crisis/non-crisis status, requires a large amount of human resources, and the processing process is not standardized and prone to errors.
近年来，在围术期管理中，基于优化生理学指标的围术期管理方法有了大量应用。如Li等人(BMJ Open 2019；9:e025337)通过观察脊椎手术中的血压、心跳等生理数据的AUC指标，发现优化的生理学指标区间和手术预后有直接关系。Helmerhorst等人(Care Med 2017；45:187-195)则观察到，PaO2数据超过上限时的高氧(Hyperoxia)指标与重症中的结局数据有相关性。在上述观察的基础上，Meng等人(PCT/CN2021/108718)使用选自侧腹肌和/或前臂的肉组织氧(SmtO2)，结合血流动力学体征包括心排量(CO)、血压(BP)等，通过计算AUC指标及持续时间，进行围术期管理，从而降低病人术后恶心和呕吐(PONV)的患病率。Meng等人(PCT/CN2021/108716)则通过对血流动力学数据包括心排量(CO)、血压(BP)、每搏率(SV)、心率(HR)、***性血管阻力(SVR)、每搏量变异度(SVV)进行基线值和上下限范围比较，从而对外科手术的病人进行高质量的围术期管理。然而在上述文献为代表的围术期管理方法中，用户均需要从多个监护仪器中获取多种高质量的生理数据，通过计算后得到优化生理学指标，从而进行高质量的围术期管理。In recent years, perioperative management methods based on optimizing physiological indicators have been widely used in perioperative management. For example, Li et al. (BMJ Open 2019;9:e025337) observed the AUC index of physiological data such as blood pressure and heartbeat during spinal surgery and found that the optimized physiological index interval is directly related to the prognosis of surgery. Helmerhorst et al. (Care Med 2017; 45:187-195) observed that the hyperoxia index when the PaO2 data exceeds the upper limit is correlated with the outcome data in severe cases. On the basis of the above observations, Meng et al. (PCT/CN2021/108718) used muscle tissue oxygen (SmtO2) selected from the abdominal muscles and/or forearms, combined with hemodynamic signs including cardiac output (CO), blood pressure (BP), etc., perform perioperative management by calculating the AUC index and duration, thereby reducing the prevalence of postoperative nausea and vomiting (PONV) in patients. Meng et al. (PCT/CN2021/108716) analyzed hemodynamic data including cardiac output (CO), blood pressure (BP), stroke rate (SV), heart rate (HR), and systemic vascular resistance (SVR). , stroke volume variation (SVV) is compared with the baseline value and the upper and lower ranges, so as to provide high-quality perioperative management of surgical patients. However, in the perioperative management methods represented by the above-mentioned literature, users need to obtain a variety of high-quality physiological data from multiple monitoring instruments, and obtain optimized physiological indicators through calculation, so as to conduct high-quality perioperative management.
在围术期管理中，基于生理学指标的围术期数据管理方法已有大量应用。Lee&Jung(Nature SCieNtifiC REPORTS(2018)8:1527)描述了一种软件Vital Recorder，连接多个麻醉监护仪器，从而自动记录高分辨率、时间同步的生理数据。但是该***笼统地将所有生理数据都进行存储和显示，只适合在术后、医生进行科研时使用，无法满足围术期管理的需求。Johnson et al.(Mimic-iii,a freely accessible critical care database.Sci.data 3(2016).)通过两个医院的监护仪采集大量病人生理数据，构建了公开数据集Medical Information Mart for Intensive Care(MIMIC-III)。但是这一公开数据集也只能用于事后的数据研究使用，无法提供优化生理学指标所需的围术期数据管理。 In perioperative management, perioperative data management methods based on physiological indicators have been widely used. Lee & Jung (Nature SCieNtifiC REPORTS (2018) 8:1527) describe a software Vital Recorder that connects multiple anesthesia monitoring instruments to automatically record high-resolution, time-synchronized physiological data. However, this system generally stores and displays all physiological data. It is only suitable for use after surgery and when doctors conduct scientific research, and cannot meet the needs of perioperative management. Johnson et al. (Mimic-iii, a freely accessible critical care database.Sci.data 3(2016).) collected a large amount of patient physiological data through monitors in two hospitals and constructed a public data set Medical Information Mart for Intensive Care( MIMIC-III). However, this public data set can only be used for post-hoc data research and cannot provide the perioperative data management needed to optimize physiological indicators.
根据临床需要，医生可以从多种监护仪器中获取海量、高频生理数据。但是相关数据并没有合理分类显示，辅助归因分析，从而进行更好的围术期数据管理。According to clinical needs, doctors can obtain massive, high-frequency physiological data from a variety of monitoring instruments. However, the relevant data are not properly classified and displayed to assist in attribution analysis for better perioperative data management.
迈瑞(公开号WO2020132826A1)中披露了按照生理体征参数的相关性将所述参数分类，以获得生理体征的集合，并将该生理体征集合在切片窗显示的监测方法。但在切片窗中进行数据分类显示，仍然不直观，缺少临床所需的数据分层。Mindray (publication number WO2020132826A1) discloses a monitoring method that classifies physiological sign parameters according to their correlation to obtain a set of physiological signs, and displays the set of physiological signs in a slice window. However, data classification display in the slice window is still not intuitive and lacks the data stratification required for clinical practice.
陈涌鸣(公开号CN 112331325 A)公开了一种人工智能下基本生命支持决策***。其中包括监测循环***、呼吸***、体温、麻醉深度、肌松等模块，决策***根据监测数据，自动识别生命体征的异常情况，做出原因分析判断，生成决策方案。其中将监测指标进行分类，决策***将异常指标单独归类，并根据体征和治疗指南自动生成医疗决策。但仅在分类后进行正常值、异常值的分类，并不能进行有效地归因分析。Chen Yongming (publication number CN 112331325 A) disclosed a basic life support decision-making system under artificial intelligence. It includes modules for monitoring the circulatory system, respiratory system, body temperature, anesthesia depth, muscle relaxation and other modules. Based on the monitoring data, the decision-making system automatically identifies abnormalities in vital signs, makes cause analysis and judgment, and generates decision-making plans. Monitoring indicators are classified, and the decision-making system classifies abnormal indicators separately, and automatically generates medical decisions based on physical signs and treatment guidelines. However, only classifying normal values and abnormal values after classification cannot perform effective attribution analysis.
现有的风险评估和临床决策智能辅助***，例如华西医院(公开号CN 111009322 A)公开了一种围术期风险评估和临床智能决策辅助***，进行了围术期风险结构化评估预测，和围术期决策智能辅助。根据各种仪器设备和电子病历***的数据，进行风险事件判断和预测，提供相应干预技术方案。但是对风险事件没有多层分类判断，也没有相应的自动化处理干预过程。Existing risk assessment and clinical decision-making intelligent assistance systems, such as West China Hospital (publication number CN 111009322 A), has disclosed a perioperative risk assessment and clinical intelligent decision-making assistance system, which performs structured perioperative risk assessment and prediction, and Intelligent assistance for perioperative decision-making. Based on data from various instruments and electronic medical record systems, we can judge and predict risk events and provide corresponding intervention technical solutions. However, there is no multi-level classification judgment for risk events, and there is no corresponding automated processing and intervention process.
另外，现有监护仪器的报警***，在某一监护参数超出阈值范围时，给予报警提示，但是只限于独立参数判断，而且没有给出相应处理流程。In addition, the alarm system of existing monitoring instruments will give an alarm prompt when a certain monitoring parameter exceeds the threshold range, but it is limited to independent parameter judgment and does not provide a corresponding processing process.
因此，需要改善现有技术中的上述缺陷，将来自多种设备的海量生理数据进行有效的收集、分类处理和显示，针对具体情况呈现辅助处理流程，从而更有效地优化生理数据分析。Therefore, it is necessary to improve the above-mentioned defects in the existing technology, effectively collect, classify, process and display massive physiological data from a variety of devices, and present auxiliary processing processes according to specific situations, so as to more effectively optimize physiological data analysis.
发明内容Contents of the invention
本发明提供一种生理数据收集、处理、呈现方法，包括如下步骤：The present invention provides a physiological data collection, processing, and presentation method, which includes the following steps:
(1)获取生理数据；(1) Obtain physiological data;
(2)通过对上述生理数据的处理、分析，进行危机/非危机分类和二级分类；(2) Perform crisis/non-crisis classification and secondary classification through processing and analysis of the above physiological data;
(3)根据步骤(2)获得的分类结果，自动分析、呈现出干预辅助方案，进行干预；(3) Based on the classification results obtained in step (2), automatically analyze and present the intervention auxiliary plan for intervention;
(4)获取干预后的生理数据，分析、查看干预后的危机/非危机分类和二级分类，视情况考虑是否再次进行干预。(4) Obtain the physiological data after the intervention, analyze and review the crisis/non-crisis classification and secondary classification after the intervention, and consider whether to intervene again according to the situation.
根据本发明的实施方案，步骤(1)中，获取生理数据的方式可以使用本领域技术人员已知的方式，例如通过有线或无线(如云端)方式连接生理数据的检测仪器或监测仪器，其中所述检测仪器或监测仪器包括但不限于围术期管理监护仪器，从而获取生理数据。According to the embodiment of the present invention, in step (1), the method of obtaining physiological data can use methods known to those skilled in the art, such as connecting physiological data detection instruments or monitoring instruments through wired or wireless (such as cloud) means, where The detection instruments or monitoring instruments include but are not limited to perioperative management and monitoring instruments to obtain physiological data.
优选地，所述生理数据为有生命的人体或动物体的生理指标数据。Preferably, the physiological data is physiological index data of a living human body or animal body.
根据本发明的实施方案，所述危机与非危机的分类判定为，如不及时进行干预，将会给对象造成不可逆转的伤害，甚至死亡。According to the embodiment of the present invention, the classification judgment of crisis and non-crisis is that if no timely intervention is performed, irreversible damage or even death will be caused to the subject.
根据本发明的实施方案，步骤(2)中，所述生理数据的处理、分析包括：通过质量分析和筛选，以获取筛选后予以保留的原始生理数据，即优选生理数据；任选对优选生理数据进行优化分析，得到优化生理数据。According to the embodiment of the present invention, in step (2), the processing and analysis of the physiological data include: obtaining the original physiological data retained after screening through quality analysis and screening, that is, the preferred physiological data; optionally analyzing the preferred physiological data. The data is optimized and analyzed to obtain optimized physiological data.
根据本发明的实施方案，所述生理数据的处理、分析在生理数据质量评估模块中进行，将得到的优选生理数据送入危机/非危机分类模块进行分类，将筛掉的重复生理数据送入生理数据传输模块。 According to the embodiment of the present invention, the physiological data is processed and analyzed in the physiological data quality assessment module, the obtained optimal physiological data is sent to the crisis/non-crisis classification module for classification, and the filtered out repeated physiological data is sent to Physiological data transmission module.
例如，所述生理数据的处理、分析还可以采用中国专利第202111168482.8号中记载的生理数据的收集、管理装置、处理方法。例如，所述质量分析和筛选可以在生理数据质量分析模块中进行。例如，所述优化分析可以在生理数据优化分析模块中进行。For example, the physiological data processing and analysis can also use the physiological data collection and management device and processing method described in Chinese Patent No. 202111168482.8. For example, the quality analysis and screening can be performed in the physiological data quality analysis module. For example, the optimization analysis can be performed in the physiological data optimization analysis module.
根据本发明的实施方案，所述危机/非危机分类可以通过下述两种方法实现：According to the embodiment of the present invention, the crisis/non-crisis classification can be achieved by the following two methods:
方法一：将没有离群数据、采集质量合格的生理数据进行处理，得到目前生理数据的分析情况，如果超过阈值，则标记异常，结合多个异常标识，给出危机或者非危机状态判断和二级判断；Method 1: Process the physiological data with no outlier data and qualified collection quality to obtain the analysis of the current physiological data. If it exceeds the threshold, mark the abnormality and combine multiple abnormal flags to provide a crisis or non-crisis status judgment and two level judgment;
优选地，生理数据的处理包括：对单一生理学参数的AUC分析，对多个生理学参数之间的相关性分析；Preferably, the processing of physiological data includes: AUC analysis of a single physiological parameter, and correlation analysis between multiple physiological parameters;
方法二：将没有离群数据、采集质量合格的生理数据导入分析模型，由分析模型给出危机或者非危机状态判断和二级判断。Method 2: Import the physiological data with no outlier data and qualified collection quality into the analysis model, and the analysis model will give the crisis or non-crisis state judgment and secondary judgment.
根据本发明的实施方案，所述分析模型包括危机/非危机分类模型和二级分类模型。According to an embodiment of the present invention, the analysis model includes a crisis/non-crisis classification model and a two-level classification model.
根据本发明的实施方案，所述危机/非危机分类模型建立在归一化模型、文本处理模型、时序分析模型的基础上，所述归一化模型提供基本信息数据，所述文本处理模型提供历史数据，所述时序分析模型提供优化生理数据。According to the embodiment of the present invention, the crisis/non-crisis classification model is based on a normalized model, a text processing model, and a timing analysis model. The normalized model provides basic information data, and the text processing model provides Historical data, the time series analysis model provides optimized physiological data.
根据本发明的实施方案，所述归一化模型基于提供生理数据的对象的基本信息构建。例如，所述基本信息包括对象的出生日期、身高、体重、性别等。According to an embodiment of the present invention, the normalized model is constructed based on basic information of the subject providing physiological data. For example, the basic information includes the subject's date of birth, height, weight, gender, etc.
根据本发明的实施方案，所述文本处理模型基于提供生理数据的对象的基本情况构建。例如，所述基本情况包括疾病诊断、病史、慢性病史、药物史、PONV、手术类型、***物、ASA级别等。According to an embodiment of the present invention, the text processing model is built based on the base case of the object providing physiological data. For example, the basic information includes disease diagnosis, medical history, chronic medical history, drug history, PONV, type of surgery, anesthetic drugs, ASA level, etc.
根据本发明的实施方案，所述时序分析模型基于去除离群数据(outlier)的生理数据和时序异常数据(abnormal)分析模型构建。According to an embodiment of the present invention, the time series analysis model is constructed based on physiological data with outliers removed and a time series abnormal data (abnormal) analysis model.
进一步地，所述时序异常数据(abnormal)分析模型包括修正判断：单一参数和/或单一设备的异常数据，直接判断为异常数据；多参数、多设备的异常数据，送入危机/非危机分类模型中进行危机/非危机判断。Further, the time series abnormal data (abnormal) analysis model includes correction judgment: abnormal data of a single parameter and/or a single device is directly judged as abnormal data; abnormal data of multiple parameters and multiple devices is sent to crisis/non-crisis classification. Crisis/non-crisis judgments are made in the model.
根据本发明的实施方案，所述二级分类模型建立在归一化模型和危机/非危机分类模型的基础上。According to an embodiment of the present invention, the two-level classification model is based on a normalized model and a crisis/non-crisis classification model.
根据本发明的实施方案，所述危机分类根据循环、呼吸、神经、其它等的危险生理指标数据进行包括但不限于下述的分类：循环危机分类、呼吸危机分类、神经危机分类和其它危机分类。According to an embodiment of the present invention, the crisis classification includes but is not limited to the following classifications based on circulatory, respiratory, neurological, and other dangerous physiological indicator data: circulatory crisis classification, respiratory crisis classification, neurological crisis classification, and other crisis classifications. .
例如，所述循环危机分类包括：心跳骤停、心动过缓、室上性心动过速、室颤/室速、大出血、严重低血压、严重高血压、心肌缺血、右心衰竭等；For example, the circulatory crisis classification includes: cardiac arrest, bradycardia, supraventricular tachycardia, ventricular fibrillation/ventricular tachycardia, massive hemorrhage, severe hypotension, severe hypertension, myocardial ischemia, right heart failure, etc.;
例如，所述呼吸危机分类包括：支气管痉挛、肺栓塞、气道压异常增高、严重低氧血症、张力性气胸等；For example, the respiratory crisis classification includes: bronchospasm, pulmonary embolism, abnormally increased airway pressure, severe hypoxemia, tension pneumothorax, etc.;
例如，所述神经危机分类包括：苏醒延迟、高位腰麻等；For example, the neurological crisis classification includes: delayed awakening, high spinal anesthesia, etc.;
例如，所述其它危机分类包括：创伤抢救、过敏反应、输血反应、局麻药中毒、恶性高热、供养失败、突然停电等。For example, the other crisis categories include: trauma rescue, allergic reaction, blood transfusion reaction, local anesthetic poisoning, malignant hyperthermia, feeding failure, sudden power outage, etc.
根据本发明的实施方案，所述非危机分类根据循环、呼吸、大脑、温度等的非危险生理 指标数据进行包括但不限于下述的分类：循环非危机分类、呼吸非危机分类、大脑非危机分类和温度非危机分类；According to an embodiment of the present invention, the non-crisis classification is based on non-risk physiology of circulation, breathing, brain, temperature, etc. The indicator data includes but is not limited to the following classifications: circulation non-crisis classification, respiratory non-crisis classification, brain non-crisis classification and temperature non-crisis classification;
优选地，所述循环非危机分类包括但不限于：心率偏慢/快、血压偏低/高、容量偏低、心排量偏低/高、组织氧偏低/高；Preferably, the circulatory non-crisis classification includes but is not limited to: slow/fast heart rate, low/high blood pressure, low volume, low/high cardiac output, low/high tissue oxygen;
优选地，所述呼吸非危机分类包括但不限于：潮气量偏大、驱动压偏高、平台压偏高、气道峰压偏高、氧合偏低/高、过度通气、高碳酸血症；Preferably, the respiratory non-crisis classification includes but is not limited to: high tidal volume, high driving pressure, high plateau pressure, high peak airway pressure, low/high oxygenation, hyperventilation, and hypercapnia. ;
优选地，所述大脑非危机分类包括但不限于：脑氧偏低/高、麻醉偏深/浅、颅内压偏高、脑松弛不良、脑灌注压偏低；Preferably, the brain non-crisis classification includes but is not limited to: low/high cerebral oxygen, deep/shallow anesthesia, high intracranial pressure, poor cerebral relaxation, and low cerebral perfusion pressure;
优选地，所述温度非危机包括但不限于：体温偏低/高。Preferably, the temperature non-crisis includes but is not limited to: low/high body temperature.
根据本发明的实施方案，步骤(3)中，所述干预辅助方案包括：接入辅助治疗设备、和/或其他干预设备。According to the embodiment of the present invention, in step (3), the intervention auxiliary plan includes: accessing auxiliary treatment equipment and/or other intervention equipment.
例如，所述辅助治疗设备包括但不限于：For example, the auxiliary treatment equipment includes but is not limited to:
呼吸设备，用于呼吸生理参数的调整；Respiratory equipment for adjustment of respiratory physiological parameters;
药物设备，用于药物调整，可将注射泵连接到对象。Medication device, used for medication adjustment, that connects a syringe pump to the subject.
根据本发明的实施方案，步骤(3)中，所述自动分析在原因和干预措施分析模块中进行，该模块包含各种危机/非危机分类和二级分类的对应原因和可选择的干预措施。According to the embodiment of the present invention, in step (3), the automatic analysis is performed in the cause and intervention analysis module, which contains corresponding causes and optional intervention measures for various crisis/non-crisis classifications and secondary classifications. .
根据本发明的实施方案，步骤(3)中，当呈现的干预辅助方案欠佳时，可通过人工进行调整。According to the embodiment of the present invention, in step (3), when the intervention auxiliary plan presented is not optimal, it can be adjusted manually.
根据本发明的实施方案，步骤(3)中，所述干预可以通过人工干预或机器自动干预。其中，所述机器自动干预优选为人工确认后，机器操作干预。According to the embodiment of the present invention, in step (3), the intervention may be manual intervention or automatic machine intervention. Wherein, the automatic machine intervention is preferably machine operation intervention after manual confirmation.
根据本发明的实施方案，步骤(3)中，所述干预辅助方案通过显示装置呈现给用户。According to the embodiment of the present invention, in step (3), the intervention assistance plan is presented to the user through the display device.
本发明还提供一种显示方法，包括将危机/非危机分类结果、二级分类结果、危机/非危机下的状态、干预辅助方案和/或干预后的效果通过显示装置呈现给用户的步骤。The present invention also provides a display method, which includes the steps of presenting crisis/non-crisis classification results, secondary classification results, crisis/non-crisis status, intervention assistance plans and/or post-intervention effects to the user through a display device.
本发明还提供上述生理数据收集、处理、呈现装置，以实现上述生理数据收集、处理、呈现方法和/或显示方法。The present invention also provides the above-mentioned physiological data collection, processing, and presentation device to implement the above-mentioned physiological data collection, processing, and presentation methods and/or display methods.
根据本发明的实施方案，所述装置包括生理数据传输模块、危机/非危机分类模块、二级分类模块、原因和干预措施分析模块、干预模块和干预效果分析模块。According to an embodiment of the present invention, the device includes a physiological data transmission module, a crisis/non-crisis classification module, a secondary classification module, a cause and intervention analysis module, an intervention module and an intervention effect analysis module.
根据本发明的实施方案，所述生理数据传输模块可以包括与提供生理数据的检测仪器或监测仪器连接的元件，从而将检测仪器或监测仪器采集或储存的数据传输至所述生理数据收集、处理、呈现装置，以获取生理数据。例如，所述生理数据传输模块可以是本领域已知的有线或无线(如云端)传输模块。According to an embodiment of the present invention, the physiological data transmission module may include an element connected to a detection instrument or monitoring instrument that provides physiological data, thereby transmitting the data collected or stored by the detection instrument or monitoring instrument to the physiological data collection and processing ,present device to obtain physiological data. For example, the physiological data transmission module may be a wired or wireless (such as cloud) transmission module known in the art.
根据本发明的实施方案，所述危机/非危机分类模块用于将优选生理数据或原始生理数据进行危机和非危机的分类。According to an embodiment of the present invention, the crisis/non-crisis classification module is used to classify the preferred physiological data or raw physiological data into crisis and non-crisis.
根据本发明的实施方案，所述装置还包括生理数据质量评估模块，与所述生理数据传输模块和危机/非危机分类模块分别连接。由所述生理数据质量评估模块得到的优选生理数据送入危机/非危机分类模块进行分类，将筛掉的重复生理数据送入生理数据传输模块。According to an embodiment of the present invention, the device further includes a physiological data quality assessment module, which is respectively connected to the physiological data transmission module and the crisis/non-crisis classification module. The optimal physiological data obtained by the physiological data quality assessment module is sent to the crisis/non-crisis classification module for classification, and the filtered out repeated physiological data is sent to the physiological data transmission module.
根据本发明的实施方案，所述二级分类模块用于危机/非危机的二级数据的分类。According to an embodiment of the present invention, the secondary classification module is used for classifying crisis/non-crisis secondary data.
根据本发明的实施方案，所述原因和干预措施分析模块包含各种危机/非危机分类和二级 分类的对应原因和可选择的干预措施，向用户提供相应状态下的干预辅助方案。According to an embodiment of the invention, the cause and intervention analysis module includes various crisis/non-crisis classifications and secondary Classified corresponding causes and optional intervention measures provide users with auxiliary intervention plans in corresponding situations.
根据本发明的实施方案，所述干预模块与所述原因和干预措施分析模块连接，根据干预辅助方案，进行人工干预和/或机器自动干预。According to an embodiment of the present invention, the intervention module is connected to the cause and intervention measure analysis module, and performs manual intervention and/or automatic machine intervention according to the intervention auxiliary plan.
根据本发明的实施方案，所述干预效果分析模块与危机/非危机分类模块、二级分类模块和干预模块分别连接，用于分析干预后的效果，判断是否继续进行干预。According to the embodiment of the present invention, the intervention effect analysis module is connected to the crisis/non-crisis classification module, the secondary classification module and the intervention module respectively, and is used to analyze the effect after the intervention and determine whether to continue the intervention.
根据本发明的实施方案，所述装置还包括显示模块，用于将危机/非危机分类结果、二级分类结果、危机/非危机下的状态、干预辅助方案和/或干预后的效果通过显示装置呈现给用户。According to an embodiment of the present invention, the device further includes a display module for displaying crisis/non-crisis classification results, secondary classification results, crisis/non-crisis status, intervention assistance programs and/or post-intervention effects. The device is presented to the user.
根据本发明的实施方案，所述装置还可以包括至少一个交互模块，以允许用户控制至少一个其他模块的运行，例如其他模块的打开、关闭、暂停或继续运行；或者，所述交互模块可以允许用户处理生理数据的分类、干预措施，例如：手动修改、分类或标记上述生理数据的分类、选择或调整干预措施、和/或数据干预结果传输至其他设备。所述交互模块可以通过触摸屏或机械按键输入控制指令。According to an embodiment of the present invention, the device may further include at least one interactive module to allow the user to control the operation of at least one other module, such as opening, closing, pausing or continuing to run other modules; or, the interactive module may allow Users process the classification and intervention of physiological data, for example: manually modify, classify or mark the classification of the above physiological data, select or adjust intervention measures, and/or transmit the data intervention results to other devices. The interactive module can input control instructions through a touch screen or mechanical keys.
本领域技术人员应当理解，根据需要，所述装置可以包含额外的传输元件，以将所述分类结果、干预措施、和/或干预效果传输至其他设备。Those skilled in the art will understand that, as needed, the device may include additional transmission elements to transmit the classification results, intervention measures, and/or intervention effects to other devices.
根据本发明的实施方案，所述装置中的上述模块还可以包括一个或更多个数据储存元件，以根据各模块的需要储存分类结果、干预措施、和/或干预效果。According to embodiments of the present invention, the above-mentioned modules in the device may also include one or more data storage elements to store classification results, intervention measures, and/or intervention effects according to the needs of each module.
本发明还提供一种预防和/或治疗疾病的方法，包括使用所述生理数据收集、处理、呈现方法或显示方法预防和/或治疗疾病。The present invention also provides a method for preventing and/or treating diseases, including using the physiological data collection, processing, presenting method or display method to prevent and/or treat diseases.
本发明还提供一种围术期对象的管理方法，包括使用所述生理数据收集、处理、呈现方法或显示方法获得对象的身体状况，并根据其身体状况选择或不选择预防和/或治疗疾病的方法。The present invention also provides a management method for perioperative subjects, including using the physiological data collection, processing, presentation method or display method to obtain the physical condition of the subject, and selecting or not selecting to prevent and/or treat diseases according to the physical condition. Methods.
本发明还提供上述生理数据收集、处理、呈现方法，显示方法或生理数据收集、处理、呈现装置在生理数据分析中的用途。The present invention also provides the use of the above physiological data collection, processing and presentation methods, display methods or physiological data collection, processing and presentation devices in physiological data analysis.
本发明还提供上述生理数据收集、处理、呈现方法，生理数据显示方法或生理数据收集、处理、呈现装置在预防和/或治疗疾病中的用途。The present invention also provides the use of the above physiological data collection, processing and presentation methods, physiological data display methods or physiological data collection, processing and presentation devices in preventing and/or treating diseases.
本发明还提供上述生理数据收集、处理、呈现方法，生理数据显示方法或生理数据收集、处理、呈现装置在对象围术期管理中的用途。The present invention also provides the use of the above physiological data collection, processing and presentation methods, physiological data display methods or physiological data collection, processing and presentation devices in perioperative management of subjects.
本发明还提供上述生理数据收集、处理、呈现装置在制造用于预防和/或治疗疾病的***，或制造用于对象围术期管理的***中的用途。The present invention also provides the use of the above physiological data collection, processing, and presentation device in manufacturing a system for preventing and/or treating diseases, or manufacturing a system for perioperative management of subjects.
根据本发明的实施方案，所述围术期包括手术前、手术中和手术后的时期。According to an embodiment of the present invention, the perioperative period includes the period before surgery, during surgery and after surgery.
有益效果beneficial effects
本发明解决了面对来自多种设备的海量生理数据，如何提高效率，有的放矢，有目的性的在数据采集、处理和呈现的基础上，聚焦问题，提出清晰的解决路线，并且结合数据处理分析，反馈跟踪干预效果，从而更有效地进行危机和非危机管理的技术问题。The present invention solves the problem of how to improve efficiency in the face of massive physiological data from a variety of devices. It focuses on the problem and proposes a clear solution route based on data collection, processing and presentation in a targeted and purposeful manner, combined with data processing and analysis. , feedback tracks intervention effects, leading to more effective technical issues in crisis and non-crisis management.
具体表现在以下三个方面：This is specifically reflected in the following three aspects:
1.从多个监护仪器的海量数据中，自动按层次给出危机和非危机的判断和二级状态判断，从而减少用户人工处理、分析数据的负担和设备压力，提高效率和目的性，有利于围术期管理； 1. From the massive data of multiple monitoring instruments, crisis and non-crisis judgments and secondary status judgments are automatically given hierarchically, thereby reducing the user's burden of manual processing and analysis of data and the pressure on equipment, improving efficiency and purpose, and effectively Conducive to perioperative management;
2.根据状态判断，给出干预步骤，可进行自动化干预或者或提供辅助干预方案，从而减少用户人工工作量，利于提高医疗质量；2. Based on the status judgment, intervention steps are given, and automated intervention or auxiliary intervention programs can be provided, thereby reducing the user's manual workload and improving the quality of medical care;
3.根据干预效果的反馈跟踪，及时更新对象状态和干预步骤，闭环管理，有助于实现医疗自动化。3. Based on feedback tracking of intervention effects, timely updates of object status and intervention steps, closed-loop management, help achieve medical automation.
附图说明Description of the drawings
图1为对危机和非危机情况进行处理的生理数据收集、处理、呈现的方法流程示意图；Figure 1 is a schematic flowchart of the method for collecting, processing, and presenting physiological data for handling crisis and non-crisis situations;
图2为对危机和非危机情况进行处理的生理数据收集、处理、呈现的装置的结构示意图；Figure 2 is a schematic structural diagram of a device for collecting, processing, and presenting physiological data for handling crisis and non-crisis situations;
图3为干预模块的结构示意图；Figure 3 is a schematic structural diagram of the intervention module;
图4为对象状态监测的一种显示示意图；Figure 4 is a schematic display diagram of object status monitoring;
图5为分析模型的结构示意图；Figure 5 is a schematic structural diagram of the analysis model;
图6为危机状态分类的显示示意图；Figure 6 is a schematic diagram showing crisis status classification;
图7为非危机状态分类的显示示意图；Figure 7 is a schematic diagram showing the classification of non-crisis states;
图8为对生理指标数据的一种优化处理、分析的示例；Figure 8 is an example of optimized processing and analysis of physiological index data;
图9为对象状态监测的另一种显示示意图；Figure 9 is another display schematic diagram of object status monitoring;
图10为血流动力学金字塔的显示示意图；Figure 10 is a schematic diagram showing the hemodynamic pyramid;
图11为对象的状态监测的还一种示意图；Figure 11 is another schematic diagram of status monitoring of an object;
图12为对象的状态监测的再一种示意图；Figure 12 is another schematic diagram of status monitoring of an object;
图13为心率偏慢状态下生理指标数据优化管理的示意图；Figure 13 is a schematic diagram of the optimal management of physiological indicator data in a slow heart rate state;
图14为心率偏快状态下生理指标数据优化管理的示意图；Figure 14 is a schematic diagram of the optimal management of physiological indicator data under a fast heart rate state;
图15为血压偏低状态下生理指标数据优化管理的示意图；Figure 15 is a schematic diagram of optimal management of physiological index data under low blood pressure;
图16为血压偏高状态下生理指标数据优化管理的示意图；Figure 16 is a schematic diagram of the optimal management of physiological index data in a state of high blood pressure;
图17为容量偏低状态下生理指标数据优化管理的示意图；Figure 17 is a schematic diagram of optimal management of physiological index data in a low capacity state;
图18为心排量偏低状态下生理指标数据优化管理的示意图；Figure 18 is a schematic diagram of the optimal management of physiological index data when cardiac output is low;
图19为组织氧偏低状态下生理指标数据优化管理的示意图；Figure 19 is a schematic diagram of optimal management of physiological index data in a state of low tissue oxygen;
图20为组织氧偏高状态下生理指标数据优化管理的示意图；Figure 20 is a schematic diagram of the optimal management of physiological index data in a state of high tissue oxygen;
图21为潮气量偏大状态下生理指标数据优化管理的示意图；Figure 21 is a schematic diagram of the optimal management of physiological index data when the tidal volume is too large;
图22为气道压力偏高状态下生理指标数据优化管理的示意图；Figure 22 is a schematic diagram of optimal management of physiological index data when airway pressure is high;
图23为氧合偏低状态下生理指标数据优化管理的示意图；Figure 23 is a schematic diagram of optimal management of physiological index data in a state of low oxygenation;
图24为氧合偏高状态下生理指标数据优化管理的示意图；Figure 24 is a schematic diagram of optimal management of physiological index data in a state of high oxygenation;
图25为过度通气状态下生理指标数据优化管理的示意图；Figure 25 is a schematic diagram of optimal management of physiological index data in a hyperventilation state;
图26为高碳酸血症状态下生理指标数据优化管理的示意图；Figure 26 is a schematic diagram of optimal management of physiological index data in a state of hypercapnia;
图27为脑氧偏低状态下生理指标数据优化管理的示意图；Figure 27 is a schematic diagram of optimal management of physiological index data in a state of low cerebral oxygenation;
图28为脑氧偏高状态下生理指标数据优化管理的示意图；Figure 28 is a schematic diagram of the optimal management of physiological index data in a state of high cerebral oxygenation;
图29为麻醉偏深状态下生理指标数据优化管理的示意图；Figure 29 is a schematic diagram of the optimal management of physiological index data under deep anesthesia;
图30为麻醉偏浅状态下生理指标数据优化管理的示意图；Figure 30 is a schematic diagram of the optimal management of physiological index data under light anesthesia;
图31为颅内压偏高状态下生理指标数据优化管理的示意图；Figure 31 is a schematic diagram of optimal management of physiological index data in a state of high intracranial pressure;
图32为脑松弛不良状态下生理指标数据优化管理的示意图；Figure 32 is a schematic diagram of the optimal management of physiological index data in a state of poor cerebral relaxation;
图33为脑灌注压偏低状态下生理指标数据优化管理的示意图； Figure 33 is a schematic diagram of optimal management of physiological index data under low cerebral perfusion pressure;
图34为心跳骤停状态下生理指标数据优化管理的示意图；Figure 34 is a schematic diagram of optimal management of physiological indicator data in cardiac arrest state;
图35为心肺复苏生理指标数据优化管理的示意图；Figure 35 is a schematic diagram of optimized management of cardiopulmonary resuscitation physiological indicator data;
图36为H’s状态下生理指标数据优化管理的示意图；Figure 36 is a schematic diagram of optimal management of physiological index data in H’s state;
图37高钾血症的生理指标数据优化管理的示意图；Figure 37 Schematic diagram of optimal management of physiological indicator data for hyperkalemia;
图38T’s的生理指标数据优化管理的示意图；Figure 38 is a schematic diagram of T’s physiological index data optimization management;
图39为危机状态之心动过缓的干预示意图；Figure 39 is a schematic diagram of intervention for bradycardia in a crisis state;
图40为危机状态之室上性心动过速的干预示意图；Figure 40 is a schematic diagram of intervention for supraventricular tachycardia in a crisis state;
图41为危机状态之室颤/室速的干预示意图；Figure 41 is a schematic diagram of intervention for ventricular fibrillation/ventricular tachycardia in a crisis state;
图42为危机状态之过敏反应的干预示意图；Figure 42 is a schematic diagram of the intervention of allergic reactions in a crisis state;
图43为危机状态之支气管痉挛的干预示意图；Figure 43 is a schematic diagram of intervention for bronchospasm in a crisis state;
图44为危机状态之苏醒延迟的干预示意图；Figure 44 is a schematic diagram of intervention for delaying recovery in a crisis state;
图45为危机状态之肺栓塞的干预示意图；Figure 45 is a schematic diagram of intervention for pulmonary embolism in a crisis state;
图46为危机状态之大出血的干预示意图；Figure 46 is a schematic diagram of intervention for massive bleeding in a crisis state;
图47为危机状态之气道压异常增高的干预示意图；Figure 47 is a schematic diagram of intervention for abnormally increased airway pressure in a crisis state;
图48为危机状态之高位腰麻的干预示意图；Figure 48 is a schematic diagram of high spinal anesthesia intervention in a crisis state;
图49为危机状态之严重高血压的干预示意图；Figure 49 is a schematic diagram of intervention for severe hypertension in a crisis state;
图50为危机状态之严重低血压的干预示意图；Figure 50 is a schematic diagram of intervention for severe hypotension in a crisis state;
图51为危机状态之严重低氧血症的干预示意图；Figure 51 is a schematic diagram of intervention for severe hypoxemia in a crisis state;
图52为V/Q不匹配的干预示意图；Figure 52 is a schematic diagram of intervention for V/Q mismatch;
图53为危机状态之局部麻中毒的干预示意图；Figure 53 is a schematic diagram of the intervention of local anesthesia in a crisis state;
图54为危机状态之恶性高热的干预示意图；Figure 54 is a schematic diagram of intervention for malignant hyperthermia in a crisis state;
图55为单曲林的干预示意图；Figure 55 is a schematic diagram of the intervention in Single Forest;
图56为危机状态之心机缺氧的干预示意图；Figure 56 is a schematic diagram of the intervention of cardiac hypoxia in a crisis state;
图57为危机状态之供氧失败的干预示意图；Figure 57 is a schematic diagram of intervention for oxygen supply failure in a crisis state;
图58为危机状态之张力性气胸的干预示意图；Figure 58 is a schematic diagram of intervention for tension pneumothorax in crisis state;
图59为危机状态之突然停电的干预示意图；Figure 59 is a schematic diagram of intervention in a sudden power outage in a crisis state;
图60为危机状态之右心衰竭的干预示意图；Figure 60 is a schematic diagram of intervention for right heart failure in a crisis state;
图61为右心衰竭超声征象的示意图；Figure 61 is a schematic diagram of ultrasound signs of right heart failure;
图62为针对右心室的管理示意图；Figure 62 is a schematic diagram of management of the right ventricle;
图63为危机状态之输血反应的干预示意图；Figure 63 is a schematic diagram of intervention for transfusion reaction in crisis state;
图64为危机状态之创伤抢救的干预示意图；Figure 64 is a schematic diagram of trauma rescue intervention in a crisis state;
图65为初步调查的示意图；Figure 65 is a schematic diagram of the preliminary investigation;
图66为格拉斯哥昏迷量表；Figure 66 shows the Glasgow Coma Scale;
图67为二次调查图；Figure 67 shows the secondary survey map;
图68为手术室准备示意图；Figure 68 is a schematic diagram of operating room preparation;
图69为麻醉诱导/气道建立的示意图；Figure 69 is a schematic diagram of anesthesia induction/airway establishment;
图70为创伤性脑损伤的干预示意图；Figure 70 is a schematic diagram of intervention for traumatic brain injury;
图71为实施例3需优化的生理指标的显示示意图； Figure 71 is a schematic diagram showing the physiological indicators to be optimized in Embodiment 3;
图72为实施例3心率偏快的辅助干预方案的显示示意图。Figure 72 is a schematic diagram showing the auxiliary intervention plan for fast heart rate in Embodiment 3.
具体实施方式Detailed ways
前述的生理数据处理、收集、呈现方法具有基本如图1所示的步骤：The aforementioned physiological data processing, collection, and presentation methods have basically the steps shown in Figure 1:
S1：连接多种监护仪器，获取对象的生理数据；例如，对象的状态监测显示如图4或图9所示；S1: Connect multiple monitoring instruments to obtain the physiological data of the subject; for example, the status monitoring display of the subject is as shown in Figure 4 or Figure 9;
S2：通过对上述生理数据的处理、分析，自动将对象的状态进行危机/非危机分类和二级分类；S2: Through the processing and analysis of the above physiological data, automatically classify the subject's status into crisis/non-crisis classification and secondary classification;
所述生理数据的处理、分析包括：通过质量分析和筛选，以获取筛选后予以保留的原始生理数据，即优选生理数据；对优选生理数据进行优化分析，得到优化生理数据；The processing and analysis of the physiological data include: obtaining the original physiological data retained after screening through quality analysis and screening, that is, the optimized physiological data; performing optimization analysis on the optimized physiological data to obtain optimized physiological data;
例如，所述生理数据的处理、分析还采用中国专利第202111168482.8号中记载的生理数据的收集、管理装置、处理方法。例如，生理数据的优化如图8所示。For example, the physiological data processing and analysis also adopt the physiological data collection and management device and processing method recorded in Chinese Patent No. 202111168482.8. For example, the optimization of physiological data is shown in Figure 8.
危机和非危机的分类分别如图6、图7所示。The classification of crisis and non-crisis is shown in Figure 6 and Figure 7 respectively.
S3：根据步骤S2获得的分类结果，自动分析、呈现出干预辅助方案，人工干预或自动干预；S3: Based on the classification results obtained in step S2, automatically analyze and present the intervention auxiliary plan, manual intervention or automatic intervention;
S4：实时监测生理学参数变化情况，获取干预后的生理数据，分析、查看干预后的危机/非危机分类和二级分类，视情况考虑是否再次进行干预；S4: Monitor the changes in physiological parameters in real time, obtain physiological data after intervention, analyze and view the crisis/non-crisis classification and secondary classification after intervention, and consider whether to intervene again according to the situation;
S5：若需要再次干预，更新对象的状态判断和相应干预步骤。S5: If intervention is needed again, update the object’s status judgment and corresponding intervention steps.
所述危机/非危机分类通过下述两种方法实现：The crisis/non-crisis classification is achieved through the following two methods:
方法一：将没有离群数据、采集质量合格的生理数据进行处理，得到目前生理数据的分析情况，如果超过阈值，则标记异常，结合多个异常标识，给出危机或者非危机状态判断和二级判断；Method 1: Process the physiological data with no outlier data and qualified collection quality to obtain the analysis of the current physiological data. If it exceeds the threshold, mark the abnormality and combine multiple abnormal flags to provide a crisis or non-crisis status judgment and two level judgment;
优选地，生理数据的处理包括：对单一生理学参数的AUC分析，对多个生理学参数之间的相关性分析；Preferably, the processing of physiological data includes: AUC analysis of a single physiological parameter, and correlation analysis between multiple physiological parameters;
方法二：将没有离群数据、采集质量合格的生理数据导入分析模型，由分析模型给出危机或者非危机状态判断和二级判断；Method 2: Import the physiological data with no outlier data and qualified collection quality into the analysis model, and the analysis model will give the crisis or non-crisis state judgment and secondary judgment;
分析模型包括危机/非危机分类模型和二级分类模型；Analytical models include crisis/non-crisis classification models and two-level classification models;
如图5所示，危机/非危机分类模型建立在归一化模型、文本处理模型、时序分析模型的基础上，所述归一化模型提供基本信息数据，所述文本处理模型提供历史数据，所述时序分析模型提供优化生理数据。As shown in Figure 5, the crisis/non-crisis classification model is based on a normalization model, a text processing model, and a time series analysis model. The normalization model provides basic information data, and the text processing model provides historical data. The time series analysis model provides optimized physiological data.
归一化模型基于提供生理数据的对象的基本信息构建。例如，所述基本信息包括对象的出生日期、身高、体重、性别等。The normalized model is built based on basic information about the subject providing physiological data. For example, the basic information includes the subject's date of birth, height, weight, gender, etc.
文本处理模型基于提供生理数据的对象的基本情况构建。例如，所述基本情况包括疾病诊断、病史、慢性病史、药物史、PONV、手术类型、***物、ASA级别等。Text processing models are built on base cases of objects that provide physiological data. For example, the basic information includes disease diagnosis, medical history, chronic medical history, drug history, PONV, type of surgery, anesthetic drugs, ASA level, etc.
时序分析模型基于去除离群数据(outlier)的生理数据和时序异常数据(abnormal)分析模型构建。The time series analysis model is built based on the physiological data and time series abnormal data (abnormal) analysis model that removes outliers.
一种实施方式中，所述时序异常数据(abnormal)分析模型包括修正判断：单一参数和/或单一设备的异常数据，直接判断为异常数据；多参数、多设备的异常数据，送入危机/非危机分类模型中进行危机/非危机判断。 In one implementation, the time series abnormal data (abnormal) analysis model includes correction judgment: abnormal data of a single parameter and/or a single device are directly judged as abnormal data; abnormal data of multiple parameters and multiple devices are sent to the crisis/ Crisis/non-crisis judgments are made in the non-crisis classification model.
所述二级分类模型建立在归一化模型和危机/非危机分类模型的基础上。The two-level classification model is based on the normalized model and the crisis/non-crisis classification model.
图10-12分别呈现了需要监测、筛选和/或优化的各生理学指标、生理学状态等。Figures 10-12 respectively present various physiological indicators, physiological states, etc. that need to be monitored, screened and/or optimized.
图13-图38示例性列举了多种状态下生理指标参数优化管理的示意图，包括但不限于心率偏慢、心率偏快、血压偏低、血压偏高、容量偏低、心排量偏低等状态。Figures 13 to 38 illustrate schematic diagrams of optimal management of physiological index parameters in various states, including but not limited to slow heart rate, fast heart rate, low blood pressure, high blood pressure, low capacity, and low cardiac output. etc. status.
图39-图70示例性列举了多种危机状态下的辅助干预操作，包括但不限于心动过缓、室上性心动过速、室颤/室速、过敏反应等危机情况。Figures 39 to 70 illustrate auxiliary intervention operations in various crisis situations, including but not limited to bradycardia, supraventricular tachycardia, ventricular fibrillation/ventricular tachycardia, allergic reactions and other crisis situations.
上述方法可以在下述装置中进行：The above method can be carried out in the following equipment:
所述装置包括生理数据传输模块、危机/非危机分类模块、二级分类模块、原因和干预措施分析模块、干预模块和干预效果分析模块；The device includes a physiological data transmission module, a crisis/non-crisis classification module, a secondary classification module, a cause and intervention analysis module, an intervention module and an intervention effect analysis module;
一种实施方式中，所述生理数据传输模块包括与提供生理数据的检测仪器或监测仪器连接的元件，从而将检测仪器或监测仪器采集或储存的数据传输至所述生理数据收集、处理、呈现装置，以获取生理数据。例如，所述生理数据传输模块可以是本领域已知的有线或无线(如云端)传输模块。In one embodiment, the physiological data transmission module includes an element connected to a detection instrument or monitoring instrument that provides physiological data, thereby transmitting the data collected or stored by the detection instrument or monitoring instrument to the physiological data collection, processing, and presentation. device to obtain physiological data. For example, the physiological data transmission module may be a wired or wireless (such as cloud) transmission module known in the art.
一种实施方式中，所述危机/非危机分类模块用于将优选生理数据或原始生理数据进行危机和非危机的分类。In one embodiment, the crisis/non-crisis classification module is used to classify the preferred physiological data or raw physiological data into crisis and non-crisis.
一种实施方式中，所述装置还包括生理数据质量评估模块，与所述生理数据传输模块和危机/非危机分类模块分别连接。由所述生理数据质量评估模块得到的优选生理数据送入危机/非危机分类模块进行分类，将筛掉的重复生理数据送入生理数据传输模块。In one embodiment, the device further includes a physiological data quality assessment module, which is respectively connected to the physiological data transmission module and the crisis/non-crisis classification module. The optimal physiological data obtained by the physiological data quality assessment module is sent to the crisis/non-crisis classification module for classification, and the filtered out repeated physiological data is sent to the physiological data transmission module.
所述二级分类模块用于危机/非危机的二级数据的分类；The secondary classification module is used to classify crisis/non-crisis secondary data;
所述原因和干预措施分析模块包含各种危机/非危机分类和二级分类的对应原因和可选择的干预措施，向用户提供相应状态下的干预辅助方案；The cause and intervention measure analysis module contains corresponding causes and optional intervention measures for various crisis/non-crisis classifications and secondary classifications, and provides users with intervention auxiliary plans in corresponding states;
所述干预模块与所述原因和干预措施分析模块连接，根据干预辅助方案，进行人工干预和/或机器自动干预；如图3所示；The intervention module is connected to the cause and intervention measure analysis module, and performs manual intervention and/or automatic machine intervention according to the intervention auxiliary plan; as shown in Figure 3;
所述干预效果分析模块与危机/非危机分类模块、二级分类模块和干预模块分别连接，用于分析干预后的效果，判断是否继续进行干预；The intervention effect analysis module is connected to the crisis/non-crisis classification module, the secondary classification module and the intervention module respectively, and is used to analyze the effect after the intervention and determine whether to continue the intervention;
一种实施方式中，所述装置还包括显示模块，用于将危机/非危机分类结果、二级分类结果、危机/非危机下的状态、干预辅助方案和/或干预后的效果通过显示装置呈现给用户；In one embodiment, the device further includes a display module for displaying crisis/non-crisis classification results, secondary classification results, crisis/non-crisis status, intervention auxiliary plans and/or post-intervention effects through the display device. presented to the user;
一种实施方式中，所述装置还可以包括至少一个交互模块，以允许用户控制至少一个其他模块的运行，例如其他模块的打开、关闭、暂停或继续运行；或者，所述交互模块可以允许用户处理生理数据的分类、干预措施，例如：手动修改、分类或标记上述生理数据的分类、选择或调整干预措施、和/或数据干预结果传输至其他设备。所述交互模块可以通过触摸屏或机械按键输入控制指令。In one embodiment, the device may further include at least one interactive module to allow the user to control the operation of at least one other module, such as opening, closing, pausing or continuing to run other modules; or, the interactive module may allow the user Process the classification and intervention of physiological data, for example: manually modify, classify or mark the classification of the above physiological data, select or adjust the intervention, and/or transmit the data intervention results to other devices. The interactive module can input control instructions through a touch screen or mechanical keys.
本领域技术人员应当理解，根据需要，所述装置可以包含额外的传输元件，以将所述分类结果、干预措施、和/或干预效果传输至其他设备。Those skilled in the art will understand that, as needed, the device may include additional transmission elements to transmit the classification results, intervention measures, and/or intervention effects to other devices.
一种实施方式中，所述装置中的上述模块还包括一个或更多个数据储存元件，以根据各模块的需要储存分类结果、干预措施、和/或干预效果。In one embodiment, the above-mentioned modules in the device further include one or more data storage components to store classification results, intervention measures, and/or intervention effects according to the needs of each module.
在一种实施方式中，上述方法在图2所示的装置中进行。In one embodiment, the above method is performed in the device shown in FIG. 2 .
一种预防和/或治疗疾病的方法，包括使用所述生理数据收集、处理、呈现方法或显示方 法预防和/或治疗疾病。A method for preventing and/or treating diseases, including using the physiological data collection, processing, presentation method or display method prevent and/or treat disease.
一种围术期对象的管理方法，包括使用所述生理数据收集、处理、呈现方法或显示方法获得对象的身体状况，并根据其身体状况选择或不选择预防和/或治疗疾病的方法。A method for managing perioperative subjects, including using the physiological data collection, processing, presentation method or display method to obtain the physical condition of the subject, and selecting or not selecting methods to prevent and/or treat diseases according to the physical condition.
上述生理数据收集、处理、呈现方法，显示方法或生理数据收集、处理、呈现装置在生理数据分析中的用途。The use of the above physiological data collection, processing and presentation method, display method or physiological data collection, processing and presentation device in physiological data analysis.
上述生理数据收集、处理、呈现方法，生理数据显示方法或生理数据收集、处理、呈现装置在预防和/或治疗疾病中的用途。The use of the above physiological data collection, processing, and presentation methods, physiological data display methods, or physiological data collection, processing, and presentation devices in preventing and/or treating diseases.
上述生理数据收集、处理、呈现方法，生理数据显示方法或生理数据收集、处理、呈现装置在对象围术期管理中的用途。The use of the above physiological data collection, processing and presentation method, physiological data display method or physiological data collection, processing and presentation device in perioperative management of subjects.
上述生理数据收集、处理、呈现装置在制造用于预防和/或治疗疾病的***，或制造用于对象围术期管理的***中的用途。Use of the above physiological data collection, processing, and presentation device in manufacturing a system for preventing and/or treating diseases, or manufacturing a system for perioperative management of subjects.
围术期包括手术前、手术中和手术后的时期。The perioperative period includes the period before, during, and after surgery.
下文将结合具体实施例对本发明的技术方案做更进一步的详细说明。应当理解，下列实施例仅为示例性地说明和解释本发明，而不应被解释为对本发明保护范围的限制。凡基于本发明上述内容所实现的技术均涵盖在本发明旨在保护的范围内。The technical solution of the present invention will be further described in detail below with reference to specific embodiments. It should be understood that the following examples are only illustrative and explain the present invention and should not be construed as limiting the scope of the present invention. All technologies implemented based on the above contents of the present invention are covered by the scope of protection intended by the present invention.
除非另有说明，以下实施例中使用的原料和试剂均为市售商品，或者可以通过已知方法制备。Unless otherwise stated, the raw materials and reagents used in the following examples are commercially available or can be prepared by known methods.
实施例1Example 1
1.采集各个监护仪监测的生理学参数；1. Collect the physiological parameters monitored by each monitor;
2.对生理学参数进行质量分析，将没有outlier，采集质量合格的生理学参数数据进行处理，比如，单一生理学参数的AUC分析，多个生理学参数之间的相关性分析；2. Perform quality analysis on physiological parameters. There will be no outlier, and physiological parameter data with qualified quality will be collected for processing, such as AUC analysis of a single physiological parameter and correlation analysis between multiple physiological parameters;
3.得到目前生理学参数分析情况，如果超过阈值，则给出异常标识(红色或者报警)；3. Obtain the current physiological parameter analysis, and if it exceeds the threshold, an abnormality indicator (red or alarm) will be given;
4.结合多个异常标识，给出危机或者非危机状态判断和二级判断(“优理”对应非危机，“危机”对应危机(如图4所示)。4. Combine multiple abnormal signs to provide crisis or non-crisis status judgments and secondary judgments ("Excellent" corresponds to non-crisis, and "Crisis" corresponds to crisis (as shown in Figure 4).
实施例2Example 2
1.采集各个监护仪监测的生理学参数；1. Collect the physiological parameters monitored by each monitor;
2.对生理学参数进行质量分析，没有outlier，采集质量合格的生理学参数数据，导入“分析模型”(如图5所示)中，进行分类判断；2. Perform quality analysis on physiological parameters without outlier, collect physiological parameter data with qualified quality, import it into the "analysis model" (as shown in Figure 5), and make classification judgments;
3.模型给出危机或者非危机状态判断和二级判断(“优理”对应非危机，“危机”对应危机。3. The model provides crisis or non-crisis state judgments and secondary judgments ("Excellent Reason" corresponds to non-crisis, and "Crisis" corresponds to crisis.
实施例3Example 3
1.自动或者手动判断病人状态为“心率偏快”(图71)；1. Automatically or manually determine the patient's status as "fast heart rate" (Figure 71);
2.给出干预步骤，干预步骤自动处理，或提供辅助干预方案(图72)；2. Provide intervention steps, automatically process the intervention steps, or provide auxiliary intervention plans (Figure 72);
3.同时实时监测与此状态相关的生理学参数(比如AUC)，反馈干预效果；3. At the same time, monitor the physiological parameters related to this state (such as AUC) in real time and provide feedback on the intervention effect;
4.根据干预效果反馈，进行判断病人状态；4. Based on the intervention effect feedback, judge the patient’s status;
4.1如果病人状态没变，但是有一定的干预效果，则调整干预步骤；4.1 If the patient's condition does not change but there is a certain intervention effect, adjust the intervention steps;
4.2如果病人状态发生改变，则切换病人状态，回到第1步。4.2 If the patient status changes, switch the patient status and return to step 1.
上述方法解决了面对来自多种设备的海量生理数据，如何提高效率，有的放矢，有目的 性的在数据采集、处理和呈现的基础上，聚焦问题，提出清晰的解决路线，并且结合数据处理分析，反馈跟踪干预效果，从而更有效地进行危机和非危机管理的技术问题。The above method solves the problem of how to improve efficiency and be targeted and purposeful in the face of massive physiological data from multiple devices. Based on the collection, processing and presentation of data, we focus on the problem, propose a clear solution route, and combine it with data processing and analysis to provide feedback and track the intervention effect, so as to more effectively manage technical issues of crisis and non-crisis management.
以上对本发明的实施方式进行了示例性的说明。但是，本申请的保护范围不拘囿于上述示例性的实施方式。凡在本发明的精神和原则之内，本领域技术人员所作出的任何修改、等同替换、改进等，均应当包含在本发明的保护范围之内。 The above has provided an exemplary description of the embodiments of the present invention. However, the scope of protection of the present application is not limited to the above-described exemplary embodiments. Within the spirit and principles of the present invention, any modifications, equivalent substitutions, improvements, etc. made by those skilled in the art shall be included in the protection scope of the present invention.

Claims (10)

  1. 一种生理数据收集、处理、呈现方法，其特征在于，所述方法包括如下步骤：A physiological data collection, processing, and presentation method, characterized in that the method includes the following steps:
    (1)获取生理数据；(1) Obtain physiological data;
    (2)通过对上述生理数据的处理、分析，进行危机/非危机分类和二级分类；(2) Perform crisis/non-crisis classification and secondary classification through processing and analysis of the above physiological data;
    (3)根据步骤(2)获得的分类结果，自动分析、呈现出干预辅助方案，进行干预；(3) Based on the classification results obtained in step (2), automatically analyze and present the intervention auxiliary plan for intervention;
    (4)获取干预后的生理数据，分析、查看干预后的危机/非危机分类和二级分类，视情况考虑是否再次进行干预。(4) Obtain the physiological data after the intervention, analyze and review the crisis/non-crisis classification and secondary classification after the intervention, and consider whether to intervene again according to the situation.
  2. 根据权利要求1所述的方法，其特征在于，步骤(2)中，所述生理数据的处理、分析包括：通过质量分析和筛选，以获取筛选后予以保留的原始生理数据，即优选生理数据；任选对优选生理数据进行优化分析，得到优化生理数据。The method according to claim 1, characterized in that in step (2), the processing and analysis of the physiological data include: through quality analysis and screening, to obtain the original physiological data retained after screening, that is, the preferred physiological data ; Optionally perform optimization analysis on the preferred physiological data to obtain optimized physiological data.
    优选地，所述生理数据的处理、分析在生理数据质量评估模块中进行，将得到的优选生理数据送入危机/非危机分类模块进行分类，将筛掉的重复生理数据送入生理数据传输模块。Preferably, the physiological data is processed and analyzed in the physiological data quality assessment module, the obtained preferred physiological data is sent to the crisis/non-crisis classification module for classification, and the filtered out repeated physiological data is sent to the physiological data transmission module. .
  3. 根据权利要求1或2所述的方法，其特征在于，所述危机/非危机分类通过下述两种方法实现：The method according to claim 1 or 2, characterized in that the crisis/non-crisis classification is achieved by the following two methods:
    方法一：将没有离群数据、采集质量合格的生理数据进行处理，得到目前生理数据的分析情况，如果超过阈值，则标记异常，结合多个异常标识，给出危机或者非危机状态判断和二级判断；Method 1: Process the physiological data with no outlier data and qualified collection quality to obtain the analysis of the current physiological data. If it exceeds the threshold, mark the abnormality and combine multiple abnormal flags to provide a crisis or non-crisis status judgment and two level judgment;
    优选地，生理数据的处理包括：对单一生理学参数的AUC分析，对多个生理学参数之间的相关性分析；Preferably, the processing of physiological data includes: AUC analysis of a single physiological parameter, and correlation analysis between multiple physiological parameters;
    方法二：将没有离群数据、采集质量合格的生理数据导入分析模型，由分析模型给出危机或者非危机状态判断和二级判断。Method 2: Import the physiological data with no outlier data and qualified collection quality into the analysis model, and the analysis model will give the crisis or non-crisis state judgment and secondary judgment.
  4. 根据权利要求3所述的方法，其特征在于，所述分析模型包括危机/非危机分类模型和二级分类模型。The method of claim 3, wherein the analysis model includes a crisis/non-crisis classification model and a two-level classification model.
    优选地，所述危机/非危机分类模型建立在归一化模型、文本处理模型、时序分析模型的基础上，所述归一化模型提供基本信息数据，所述文本处理模型提供历史数据，所述时序分析模型提供优化生理数据。Preferably, the crisis/non-crisis classification model is based on a normalization model, a text processing model, and a time series analysis model. The normalization model provides basic information data, and the text processing model provides historical data. The time series analysis model described above provides optimized physiological data.
    优选地，所述归一化模型基于提供生理数据的对象的基本信息构建。Preferably, the normalized model is constructed based on basic information of the subject providing physiological data.
    优选地，所述文本处理模型基于提供生理数据的对象的基本情况构建。Preferably, the text processing model is built based on the base case of the object providing physiological data.
    优选地，所述时序分析模型基于去除离群数据的生理数据和时序异常数据分析模型构建。Preferably, the time series analysis model is constructed based on the physiological data and time series abnormal data analysis model with outlier data removed.
  5. 根据权利要求4所述的方法，其特征在于，所述时序异常数据分析模型包括修正判断：单一参数和/或单一设备的异常数据，直接判断为异常数据；多参数、多设备的异常数据，送入危机/非危机分类模型中进行危机/非危机判断。The method according to claim 4, characterized in that the time series abnormal data analysis model includes correction judgment: abnormal data of a single parameter and/or a single device is directly judged as abnormal data; abnormal data of multiple parameters and multiple devices, Send it to the crisis/non-crisis classification model for crisis/non-crisis judgment.
    优选地，所述二级分类模型建立在归一化模型和危机/非危机分类模型的基础上。Preferably, the two-level classification model is based on a normalized model and a crisis/non-crisis classification model.
  6. 根据权利要求1-5任一项所述的方法，其特征在于，所述危机分类包括但不限于对造成下述危机情况的生理数据进行分类：循环危机分类、呼吸危机分类、神经危机分类和其它危机分类。 The method according to any one of claims 1 to 5, wherein the crisis classification includes but is not limited to classifying physiological data causing the following crisis situations: circulatory crisis classification, respiratory crisis classification, neurological crisis classification and Other crisis categories.
    优选地，所述非危机分类包括但不限于对属于下述非危机情况的生理数据进行分类：循环非危机分类、呼吸非危机分类、大脑非危机分类和温度非危机分类。Preferably, the non-crisis classification includes, but is not limited to, classifying physiological data belonging to the following non-crisis situations: circulation non-crisis classification, respiratory non-crisis classification, brain non-crisis classification and temperature non-crisis classification.
    优选地，步骤(3)中，所述干预辅助方案包括：接入辅助治疗设备、和/或其他干预设备。Preferably, in step (3), the intervention auxiliary plan includes: accessing auxiliary treatment equipment and/or other intervention equipment.
    优选地，步骤(3)中，所述自动分析在原因和干预措施分析模块中进行，该模块包含各种危机/非危机分类和二级分类的对应原因和可选择的干预措施。Preferably, in step (3), the automatic analysis is performed in a cause and intervention analysis module, which contains corresponding causes and optional intervention measures for various crisis/non-crisis classifications and secondary classifications.
    优选地，步骤(3)中，当呈现的干预辅助方案欠佳时，可通过人工进行调整。Preferably, in step (3), when the intervention auxiliary plan presented is not optimal, it can be adjusted manually.
    优选地，步骤(3)中，所述干预辅助方案通过显示装置呈现给用户。Preferably, in step (3), the intervention assistance plan is presented to the user through a display device.
  7. 一种显示方法，其特征在于，包括将危机/非危机分类结果、二级分类结果、危机/非危机下的状态、干预辅助方案和/或干预后的效果通过显示装置呈现给用户的步骤。A display method, which is characterized by including the step of presenting crisis/non-crisis classification results, secondary classification results, crisis/non-crisis status, intervention auxiliary plans and/or post-intervention effects to the user through a display device.
  8. 一种生理数据收集、处理、呈现装置，其特征在于，所述装置能够实现权利要求1-6任一项所述的生理数据收集、处理、呈现方法和/或权利要求7所述的显示方法。A physiological data collection, processing, and presentation device, characterized in that the device can implement the physiological data collection, processing, and presentation method described in any one of claims 1 to 6 and/or the display method described in claim 7 .
    优选地，所述装置包括生理数据传输模块、危机/非危机分类模块、二级分类模块、原因和干预措施分析模块、干预模块和干预效果分析模块。Preferably, the device includes a physiological data transmission module, a crisis/non-crisis classification module, a secondary classification module, a cause and intervention analysis module, an intervention module and an intervention effect analysis module.
    优选地，所述危机/非危机分类模块用于将优选生理数据或原始生理数据进行危机和非危机的分类。Preferably, the crisis/non-crisis classification module is used to classify the preferred physiological data or raw physiological data into crisis and non-crisis.
    优选地，所述装置还包括生理数据质量评估模块，与所述生理数据传输模块和危机/非危机分类模块分别连接。优选由所述生理数据质量评估模块得到的优选生理数据送入危机/非危机分类模块进行分类，将筛掉的重复生理数据送入生理数据传输模块。Preferably, the device further includes a physiological data quality assessment module, which is respectively connected to the physiological data transmission module and the crisis/non-crisis classification module. Preferably, the preferred physiological data obtained by the physiological data quality assessment module is sent to the crisis/non-crisis classification module for classification, and the filtered out repeated physiological data is sent to the physiological data transmission module.
    优选地，所述二级分类模块用于危机/非危机的二级数据的分类。Preferably, the secondary classification module is used for classifying crisis/non-crisis secondary data.
    优选地，所述原因和干预措施分析模块包含各种危机/非危机分类和二级分类的对应原因和可选择的干预措施，向用户提供相应状态下的干预辅助方案。Preferably, the cause and intervention measure analysis module includes corresponding causes and optional intervention measures for various crisis/non-crisis classifications and secondary classifications, and provides users with intervention assistance plans in corresponding states.
    优选地，所述干预模块与所述原因和干预措施分析模块连接，根据干预辅助方案，进行人工干预和/或机器自动干预。Preferably, the intervention module is connected to the cause and intervention measure analysis module, and performs manual intervention and/or automatic machine intervention according to the intervention assistance plan.
    优选地，所述干预效果分析模块与危机/非危机分类模块、二级分类模块和干预模块分别连接，用于分析干预后的效果，判断是否继续进行干预。Preferably, the intervention effect analysis module is connected to the crisis/non-crisis classification module, the secondary classification module and the intervention module respectively, and is used to analyze the effect after the intervention and determine whether to continue the intervention.
    优选地，所述装置还包括显示模块，用于将危机/非危机分类结果、二级分类结果、危机/非危机下的状态、干预辅助方案和/或干预后的效果通过显示装置呈现给用户。Preferably, the device further includes a display module for presenting crisis/non-crisis classification results, secondary classification results, crisis/non-crisis status, intervention assistance programs and/or post-intervention effects to the user through the display device .
    优选地，所述装置还可以包括至少一个交互模块，以允许用户控制至少一个其他模块的运行。Preferably, the device may further include at least one interactive module to allow the user to control the operation of at least one other module.
    优选地，所述装置中的上述模块还包括一个或更多个数据储存元件，以根据各模块的需要储存分类结果、干预措施、和/或干预效果。Preferably, the above-mentioned modules in the device further include one or more data storage elements to store classification results, intervention measures, and/or intervention effects according to the needs of each module.
  9. 权利要求1-6任一项所述的生理数据收集、处理、呈现方法，权利要求7所述的显示方法或权利要求8所述的生理数据收集、处理、呈现装置在生理数据分析中的用途。The physiological data collection, processing and presentation method according to any one of claims 1 to 6, the display method according to claim 7 or the use of the physiological data collection, processing and presentation device according to claim 8 in physiological data analysis. .
  10. 权利要求8所述的生理数据收集、处理、呈现装置在制造用于预防和/或治疗疾病的***，或制造用于对象围术期管理的***中的用途。 The use of the physiological data collection, processing, and presentation device according to claim 8 in manufacturing a system for preventing and/or treating diseases, or manufacturing a system for perioperative management of subjects.
PCT/CN2023/087635 2022-04-11 2023-04-11 Physiological data collecting apparatus and method, physiological data processing apparatus and method and physiological data presenting apparatus and method for handling crisis and non-crisis situations WO2023198065A1 (en)

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