WO2023207793A1 - Procédé d'interaction homme-ordinateur pour une prise de décision de santé, dispositif et système - Google Patents

Procédé d'interaction homme-ordinateur pour une prise de décision de santé, dispositif et système Download PDF

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Publication number
WO2023207793A1
WO2023207793A1 PCT/CN2023/089807 CN2023089807W WO2023207793A1 WO 2023207793 A1 WO2023207793 A1 WO 2023207793A1 CN 2023089807 W CN2023089807 W CN 2023089807W WO 2023207793 A1 WO2023207793 A1 WO 2023207793A1
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intervention
user
computer
plan
data
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PCT/CN2023/089807
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English (en)
Chinese (zh)
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吴运良
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吴运良
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • 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
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • 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

Definitions

  • the invention relates to a human-computer interaction method, equipment, storage medium and system for health decision-making, and belongs to the technical field of human-computer interaction in medical and health systems.
  • the present invention proposes a human-computer interaction method for health decision-making, which is user-centered, predicts preliminary diagnosis data based on the user's basic data, and combines the preliminary diagnosis with the doctor's The data improves the user's whole-person health data.
  • the computer and the doctor collaboratively process the user's whole-person health data, generate an intervention plan and match the intervention measures to the user.
  • the user selects the intervention measures and feeds back the treatment evaluation to the computer, forming a closed loop. system.
  • the present invention provides a human-computer interaction method for health decision-making, which is characterized by including the following steps:
  • the computer obtains the user's basic data in real time, and generates preliminary diagnostic data based on the user's basic data and sends it to the server;
  • the doctor logs in to the human-machine interface obtains at least one set of user basic data and preliminary diagnosis data from the server through the human-machine interface, processes it, generates corresponding user whole-person health data and returns it to the computer;
  • the computer generates a preliminary intervention plan based on the user's whole-person health data, and determines whether the user's whole-person health data is imbalanced. When the user's whole-person health data is imbalanced, the preliminary intervention plan is sent to the server;
  • the doctor logs in to the human-machine interface, obtains the preliminary intervention plan corresponding to the user from the server through the human-machine interface, and confirms it, and after confirmation, generates the final intervention plan and returns it to the computer;
  • the computer matches the intervention measures based on the final intervention plan and submits it to the user;
  • the final intervention plan implemented by the user and the effect of the intervention measures are evaluated, and the evaluation results are generated and submitted to the computer.
  • the computer incorporates the evaluation results into the user's basic data.
  • the processing process is specifically: according to the user basic data Correct, amend and improve preliminary diagnostic data.
  • the steps for the computer to generate a preliminary intervention plan based on the user's overall health data are specifically:
  • the intervention plan contains a number of sequential nodes, and each node has an optional intervention sub-program written corresponding to it, and each node is programmed with an optional intervention sub-program. Intervention sub-programs add program characteristics and interventions available for that intervention sub-program;
  • the intervention plan is matched from the intervention plan database based on the user's health problem, and each node of the intervention plan is matched according to the user's basic data and plan characteristics, and the intervention sub-plan with a high matching degree is selected to generate the preliminary intervention plan.
  • the preliminary intervention plan corresponding to the user is obtained from the server through the human-machine interface for confirmation, and after confirmation, the final intervention plan is generated and returned to the computer, which specifically includes:
  • the preliminary intervention plan will be returned to the computer as the final intervention plan
  • the computer will return to re-acquire the user's basic data and generate preliminary diagnostic data, submit it to the doctor for reprocessing, update the user's overall health data and submit it to the doctor;
  • the doctor confirms or modifies the intervention sub-plan at any node in the preliminary intervention plan based on the user's whole-person health data, and generates the final intervention plan and returns it to the computer after all nodes are confirmed or modified.
  • the computer matches the intervention measures according to the final intervention plan and submits them to the user.
  • the steps are specifically:
  • the intervention measure information database includes multiple sub-intervention measure databases, each sub-intervention measure database corresponding to different types of intervention measures respectively;
  • the types of intervention measures available in the intervention sub-program of each node are obtained, and the intervention measures are automatically matched from the sub-intervention measure database of the corresponding type;
  • the evaluation results include the user's effectiveness evaluation and suitability evaluation of the intervention plan and the effectiveness evaluation and suitability evaluation of the intervention measures;
  • the computer adds rating labels to the corresponding intervention programs and intervention measures based on the user's evaluation results of the effects of the intervention programs and intervention measures, and incorporates the intervention programs and corresponding rating labels, as well as the intervention measures and corresponding rating labels into the user's basic data;
  • the intervention plan and corresponding scoring labels are used to provide a basis for judgment in the subsequent process of computer-generated preliminary intervention plans and matching intervention measures.
  • the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor.
  • the processor executes the program, any embodiment of the present invention is implemented.
  • the present invention provides a computer-readable storage medium on which a computer program is stored.
  • the program is executed by a processor, the human-computer interaction method for health decision-making as described in any embodiment of the present invention is implemented.
  • the present invention provides a human-computer interaction system for health decision-making, including a computer and a server communicatively connected to the computer, and also includes:
  • the basic data acquisition module is used to obtain the user's basic data in real time in the computer, and generate preliminary diagnostic data based on the user's basic data and send it to the server;
  • the preliminary diagnosis module is used for doctors to log in to the human-machine interface, obtain at least one set of user basic data and preliminary diagnosis data from the server through the human-machine interface, process it, generate corresponding user whole-person health data and return it to the computer;
  • the preliminary plan generation module is used to generate a preliminary intervention plan based on the user's whole-person health data in the computer, and determine whether the user's whole-person health data is imbalanced. When the user's whole-person health data is imbalanced, the preliminary intervention plan is sent to the server;
  • the plan confirmation module is used for doctors to log in to the human-machine interface, obtain the preliminary intervention plan corresponding to the user from the server through the human-machine interface for confirmation, and generate the final intervention plan after confirmation and return it to the computer;
  • the measure matching module is used to match intervention measures according to the final intervention plan in the computer and submit it to the user;
  • the evaluation module is used to evaluate the final intervention plan implemented by the user and the effect of the intervention measures, generate evaluation results and submit them to the computer, and the computer will incorporate the evaluation results into the user's basic data.
  • the present invention provides a life-cycle health management service system, including a collaborative management module and a human-computer interaction system for health decision-making according to any embodiment of the present invention
  • the collaborative management module obtains data from the human-computer interaction system for health decision-making and provides services to users based on the obtained data.
  • the invention is a human-computer interaction method for health decision-making, which is user-centered, predicts preliminary diagnosis data based on the user's basic data, and completes the user's whole-person health data through the doctor's combination with the preliminary diagnosis data.
  • the user's whole-person health data is collaboratively processed, an intervention plan is generated and the matching intervention measures are provided to the user.
  • the user selects the intervention measures and feeds back the treatment evaluation to the computer, forming a closed-loop system.
  • the present invention is a human-computer interaction method for health decision-making. It uses a multi-disciplinary diagnostic model based on machine learning to replace humans to a certain extent and closely integrate humans and machines. It can carry out early diagnosis and preventive treatment, and achieve precise prevention and precise treatment of diseases. .
  • the present invention is a human-computer interaction method for health decision-making. It processes and outputs new basic data in real time. It continuously iterates so that people and intervention implementation gradually form a balance and remain in a dynamic matching and intervention state. Based on data and doctor collaboration, it provides Full-process health management outside of emergency care gives users the decision-making power for medical health intervention; it makes medical system intervention a time-point or staged special form of intervention, and users know the intervention plan and the necessity, value and effectiveness of the intervention measures in advance. and prognostic effects.
  • Figure 1 is a flow chart of a human-computer interaction method for health decision-making in an embodiment of the present invention
  • Figure 2 is an example diagram of a human-computer interaction system for health decision-making in an embodiment of the present invention
  • Figure 3 is an example diagram of a life cycle health management service system in an embodiment of the present invention.
  • Embodiment 1 Referring to Figure 1, a human-computer interaction method for health decision-making includes the following steps:
  • the computer obtains the user's basic data in real time, and generates preliminary diagnosis data based on the user's basic data and sends it to the server.
  • the preliminary diagnosis data generated in this step is used to assist the doctor in diagnosis.
  • Data acquisition can be multi-dimensional, such as collecting panoramic data from users’ daily life information, environmental information, wearable devices, physical examinations, diagnosis and treatment, etc.;
  • S102 The doctor logs in to the human-machine interface, optionally obtains at least a set of user basic data and preliminary diagnosis data from the server through the human-machine interface for processing, and corrects and confirms the preliminary diagnosis data based on the user basic data.
  • S103 Cyclically receive later daily health care data, health imbalance data and post-intervention user evaluation results, and loop steps S101 and S102 to dynamically output the corresponding user whole-person health data and return it to the computer.
  • the computer generates a preliminary intervention plan based on the user's whole-person health data, and determines whether the user's whole-person health data is imbalanced.
  • the preliminary intervention plan is sent to the server.
  • the method for determining whether the user's whole-person health data is imbalanced includes receiving corresponding values of the user's perceived symptoms, wearable device data, doctor observation data and monitoring data that deviate to a threshold, and abnormal data that will be generated by computer correlation inference predictions;
  • step S202 The doctor logs in to the human-machine interface and obtains the preliminary intervention plan corresponding to the user from the server through the human-machine interface for confirmation.
  • the doctor's confirmation process proceeds to step S203. If the doctor needs to re-collect user data for judgment, then return to step S102.
  • S3 The computer matches the intervention measures according to the final intervention plan and submits it to the user.
  • S4 The user evaluates the final intervention plan and intervention measures implemented, generates evaluation results and submits them to the computer, and the computer incorporates the evaluation results into the user's basic data.
  • steps S1 to S4 multiple rounds of cyclic interactions are performed until the overall data of the user's health status or imbalance status is formed, as well as the corresponding preferred intervention measures.
  • the preferred intervention measures are output for use in the user's daily health care, medical and health institutions, and elderly care institutions. and other real-time health interventions.
  • the user's whole-person health data refers to the data expression of the whole-person health elements using the person as a whole system, including the carbon-based space (genes, molecules, body temperature, age, shape, etc.) of the user's human body based on the cell tissue system, the person (at least Including users, doctors) thinking humanistic social environment cognition (psychology, perception, diagnosis, decision-making, etc.), natural and man-made engineering equipment intervention factors (implementation of gradual intervention including monitoring), establish a full-time dynamic health digital portrait or further form a digital Twins constitute the basic model of the digital life system. Through iteration, the best balance of the three is achieved with the goal of increasing the self-organizing power of the human body.
  • the user's proactive perception is included in the decision-making elements; the individual is regarded as a whole, and through big data integration
  • the results are analyzed to make a systematic diagnosis, and then the doctor combines his or her own clinical experience to further analyze the patient's psychological, social, environmental, subjective wishes and other influencing factors to make intervention recommendations and formulate optional intervention plans.
  • the user-centered approach integrates medical teams to provide non-disease-based general medical services, which solves the blindness of individual selection of service institutions and the limitations of individual institutions or single-disciplinary department services, embodies the overall concept of the system, and avoids " If you have a headache, treat the head; if you have a foot pain, treat the feet.” It demonstrates the individualized pursuit of combining traditional Chinese and Western medicine and giving full play to their respective advantages, dialectical treatment, individualized treatment, and treatment of co-morbidities, forming a mechanism and model for collaboration and interaction among clinical teams of Chinese and Western medicine.
  • step S201 the computer generates a preliminary intervention plan based on the user's whole person health data.
  • the specific steps are:
  • the intervention plan contains a number of sequential nodes, and each node has an optional intervention sub-program written corresponding to it, and each node is programmed with an optional intervention sub-program. Intervention sub-programs add program characteristics and interventions available for that intervention sub-program;
  • the intervention plan is matched from the intervention plan database based on the user's health problem, and each node of the intervention plan is matched according to the user's basic data and plan characteristics, and the intervention sub-plan with a high matching degree is selected to generate the preliminary intervention plan.
  • This embodiment uses a multidisciplinary diagnostic model based on machine learning (including modern medicine, traditional and complementary medicine (including traditional Chinese medicine), and knowledge systems related to promoting physical and mental health) to replace humans to a certain extent and closely integrate humans and machines, where humans at least include Users, medical and health workers.
  • Machine learning including modern medicine, traditional and complementary medicine (including traditional Chinese medicine), and knowledge systems related to promoting physical and mental health
  • Use multidisciplinary diagnostic models to obtain regional population data from regional servers, and use epidemiological analysis methods to conduct early diagnosis and preventive treatment to achieve precise prevention and precise disease treatment.
  • the artificial intelligence system When a certain element of individual health data changes, such as the occurrence of symptoms, physical signs, age, environment, psychology and other dynamic factors from multiple sources, the artificial intelligence system immediately analyzes and processes it to form real-time updated individual health data. In other words, the consumer Changes in data mean that individuals trigger service requests to the decision-making system. Similarly, when a certain queue parameter of a regional population changes, the artificial intelligence system immediately processes it and integrates biological big data, clinical big data and health medical big data through data queue analysis to achieve regionalization, individualization and precision of disease prevention and treatment.
  • step S202 the doctor logs in to the human-machine interface, obtains the preliminary intervention plan corresponding to the user from the server through the human-machine interface for confirmation, and generates the final intervention plan after confirmation and returns to the computer steps, including:
  • the preliminary intervention plan will be returned to the computer as the final intervention plan
  • the computer will return to re-acquire the user's basic data and generate preliminary diagnostic data, submit it to the doctor for reprocessing, update the user's overall health data and submit it to the doctor;
  • the doctor confirms or modifies the intervention sub-plan at any node in the preliminary intervention plan based on the user's whole-person health data, and generates the final intervention plan and returns it to the computer after all nodes are confirmed or modified.
  • step S3 the computer matches the intervention measures according to the final intervention plan and submits them to the user.
  • the steps are specifically:
  • the intervention measure information database includes multiple sub-intervention measure databases, each sub-intervention measure database corresponding to different types of intervention measures respectively;
  • the types of intervention measures available in the intervention sub-program of each node are obtained, and the intervention measures are automatically matched from the sub-intervention measure database of the corresponding type;
  • the evaluation results include the user's effectiveness evaluation and suitability evaluation of the intervention plan and the effectiveness evaluation and suitability evaluation of the intervention measures.
  • This embodiment forms a data-based health decision-making method around the user, processes and outputs new basic data in real time, and continuously iterates so that the human body and intervention implementation gradually form a balance and continue to be in a state of dynamic matching and intervention. Based on data and doctor collaboration, it provides Full-process health management except emergency first aid, giving users the decision-making power for medical health intervention; making medical system intervention a special form of time-point or staged intervention, and users knowing the intervention plan and the necessity and value of taking intervention measures in advance effectiveness and prognosis.
  • Embodiment 2 This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, any implementation of the present invention is implemented. Human-computer interaction method for health decision-making as described in the example.
  • Embodiment 3 This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the human-computer interaction method for health decision-making as described in any embodiment of the present invention is implemented. .
  • Embodiment 4 Referring to Figure 2, this embodiment provides a human-computer interaction system for health decision-making, including a computer and a server communicatively connected to the computer, and also includes:
  • the basic data acquisition module is used to obtain the user's basic data in real time in the computer, and generate preliminary diagnosis data based on the user's basic data and send it to the server; the basic data acquisition module implements the function of step S101 in Embodiment 1, which will not be described in detail here. ;
  • the preliminary diagnosis module is used for doctors to log in to the human-machine interface, obtain at least a set of user basic data and preliminary diagnosis data from the server through the human-machine interface, process it, generate corresponding user whole-person health data and return it to the computer; the preliminary diagnosis module implements The function of step S102 in Embodiment 1 will not be described again;
  • the preliminary plan generation module is used to generate a preliminary intervention plan based on the user's whole-person health data in the computer, and determine whether the user's whole-person health data is imbalanced. When the user's whole-person health data is imbalanced, the preliminary intervention plan is sent to the server; the preliminary plan The generation module implements the function of step S201 in Embodiment 1, which will not be described again;
  • the plan confirmation module is used for doctors to log in to the human-machine interface, obtain the preliminary intervention plan corresponding to the user from the server through the human-machine interface for confirmation, and generate the final intervention plan after confirmation and return it to the computer; the plan confirmation module is implemented as in the embodiment The function of step S202 will not be described again here;
  • the measure matching module is used to match intervention measures according to the final intervention plan in the computer and submit it to the user; the measure matching module implements the function of step S3 in Embodiment 1, which will not be described again here;
  • the evaluation module is used to evaluate the final intervention plan implemented by the user and the effect of the intervention measures, generate evaluation results and submit them to the computer, and the computer incorporates the evaluation results into the user's basic data; the measure matching module implements the function of step S4 in Embodiment 1. This will not be described again.
  • Embodiment 5 Referring to Figure 3, this embodiment provides a life cycle health management service system, including a collaborative management module and a human-computer interaction system for health decision-making as described in the above Embodiment 4;
  • the collaborative management module obtains data from the human-computer interaction system for health decision-making and provides services to users based on the obtained data.

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Abstract

La présente invention se rapporte à un procédé d'interaction homme-ordinateur pour une prise de décision de santé, comprenant les étapes suivantes : un ordinateur obtient des données de base d'utilisateur en temps réel, génère des données de diagnostic préliminaires en fonction des données de base d'utilisateur, et téléverse les données de diagnostic préliminaires vers un serveur; un médecin se connecte à une interface homme-ordinateur, obtient au moins un groupe de données de base d'utilisateur et des données de diagnostic préliminaires en provenance du serveur au moyen de l'interface homme-ordinateur pour traitement, génère des données de santé de personne complètes d'utilisateur correspondantes, et renvoie les données de santé de personne complètes d'utilisateur correspondantes à l'ordinateur; l'ordinateur génère un schéma d'intervention préliminaire en fonction des données de santé de personne complètes d'utilisateur, détermine si les données de santé de personne complètes d'utilisateur sont déséquilibrées, et téléverse le schéma d'intervention préliminaire vers le serveur lorsque les données de santé de personne complètes d'utilisateur sont déséquilibrées; le médecin obtient un schéma d'intervention préliminaire correspondant à un utilisateur au moyen de l'interface homme-ordinateur pour confirmation, génère un schéma d'intervention final après confirmation, et renvoie le schéma d'intervention final à l'ordinateur; l'ordinateur associe une mesure d'intervention en fonction du schéma d'intervention final et la soumet à l'utilisateur; l'utilisateur évalue le schéma d'intervention final mis en œuvre et l'effet de la mesure d'intervention et génère un résultat d'évaluation.
PCT/CN2023/089807 2022-04-26 2023-04-21 Procédé d'interaction homme-ordinateur pour une prise de décision de santé, dispositif et système WO2023207793A1 (fr)

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CN114743669A (zh) * 2022-04-26 2022-07-12 福建福寿康宁科技有限公司 用于健康决策的人机交互方法、设备、存储介质和系统

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