Whole disease course health management system based on artificial intelligence

Abstract

The invention discloses a whole course health management system based on artificial intelligence, and the system comprises a user mobile terminal which is used for receiving a rehabilitation plan, obtaining the health data of a user through a data collection device, and transmitting the health data of the user to a background data management module in real time; the doctor mobile terminal is used for acquiring the health data of the user, performing illness state analysis on the user based on the health data of the user, generating a rehabilitation plan when the health data is abnormal, and synchronously transmitting the rehabilitation plan to the background data management module and the user mobile terminal; and the background data management module is used for realizing data interaction between the user side and the doctor side, and performing real-time analysis and risk early warning on the health data of the user based on the time sequence prediction model or / and the risk assessment model. According to the method and the system, the whole course of disease health management with collaboration, intellectualization and individuation can be provided.

Description

Technical Field

[0001] The present invention belongs to the technical field of health management, and particularly relates to a whole-course health management system based on artificial intelligence. Background Art

[0002] In the technical field of whole-course health management, the existing technologies mainly focus on the following aspects:

[0003] 1) Development status of health management platforms:

[0004] 1. Fragmented functions: Currently, there are some health management platforms on the market, such as the online appointment registration platforms of some hospitals and patient follow-up systems. These platforms usually only cover a certain stage of the medical service chain (such as pre-diagnosis appointment and post-diagnosis follow-up), lacking overall management throughout the whole course (pre-diagnosis, in-diagnosis, and post-diagnosis).

[0005] 2. Data island phenomenon: Existing health management platforms often do not have a unified data interface, and each module (such as electronic medical records, diagnosis and treatment data, and follow-up data) operates independently, resulting in the inability to integrate patients' health data and failure to form a complete patient condition management file.

[0006] 2) Application status of the patient side:

[0007] 1. Limited functions: Most of the APPs on the patient side only provide functions such as appointment registration, health information query, or simple recording. For example, viewing inspection and test reports and making online appointments with doctors through the APP, but cannot obtain personalized health guidance or dynamic health monitoring.

[0008] 2. Low compliance: Most of the existing APPs on the patient side have relatively passive functions. Patients need to manually input data or only view health information when necessary. Lack of interactive design, making it difficult to motivate patients to actively participate in health management.

[0009] 3. Lack of execution of doctor's orders: After the patient is discharged from the hospital, the execution of doctor's orders and the feedback of health data mainly rely on the patient's self-awareness, lacking a scientific monitoring and support mechanism.

[0010] 3) Development status of the doctor side

[0011] 1. Single patient management function: Most of the existing doctor-side APPs or system tools have the electronic medical record (EMR) as the core function, and mainly focus on the storage and retrieval of patients' diagnosis and treatment records. When using these tools, doctors mainly rely on manual judgment, lacking in-depth analysis and intelligent assistance for patients' health data.

[0012] 2. Lack of personalized management ability: The doctor side usually only provides basic diagnosis and treatment information of patients, and fails to generate personalized diagnosis and treatment plans or rehabilitation suggestions according to the specific conditions and recovery status of patients.

[0013] 4) Current situation of the background data management module

[0014] 1. Insufficient data integration and analysis capabilities: Existing background systems are mostly limited to data storage and retrieval functions, rather than real-time analysis. For example, the health data uploaded by patients and the diagnosis and treatment information recorded by doctors often cannot be effectively correlated, and the background lacks the ability to analyze multi-dimensional data.

[0015] 2. Low level of intelligence: The background fails to make full use of artificial intelligence technology to intelligently analyze patient data and predict risks, and doctors and patients cannot receive targeted guidance information in a timely manner.

[0016] 3. Data security risks: Some platforms lack sufficient technical means in terms of data security, and there is a risk of leakage of patients' privacy data during storage and transmission.

[0017] Although the existing whole-course health management technologies have alleviated the shortage of medical resources and optimized the medical treatment process to a certain extent, the above problems still exist.

[0018] Therefore, how to provide users with a collaborative, intelligent and personalized whole-course health management has become an urgent problem to be solved in the existing technology. Summary of the Invention

[0019] The purpose of the present invention is to provide an artificial intelligence-based whole-course health management system to solve the problems existing in the prior art.

[0020] To achieve the above purpose, the present invention adopts the following technical solutions:

[0021] The present invention provides an artificial intelligence-based whole-course health management system, including:

[0022] A user mobile terminal, configured to receive a rehabilitation plan and obtain the user's health data through a data acquisition device, and transmit the user's health data to the background data management module in real time;

[0023] A doctor mobile terminal, configured to obtain the user's health data, analyze the user's condition based on the user's health data, generate a rehabilitation plan when the health data is abnormal, and synchronously transmit the rehabilitation plan to the background data management module and the user mobile terminal;

[0024] A background data management module, configured to realize data interaction between the user side and the doctor side, and perform real-time analysis and risk warning on the user's health data based on a time series prediction model or / and a risk assessment model;

[0025] Among them, performing real-time analysis and risk warning on the user's health data based on a time series prediction model includes:

[0026] Based on the acquired health data of the user, preprocessing the health data of the user, wherein the preprocessing is to remove abnormal data and fill in missing data;

[0027] Based on the pre-processed user health data, the data fluctuation range of the health data within a preset time period in the future is predicted through the time series prediction model;

[0028] When it is detected that the data fluctuation range of health data in the future preset time period exceeds the preset data fluctuation range, a risk warning is triggered. The background data management module sends the risk warning information to the user's mobile terminal and the doctor's mobile terminal, and provides health advice plans;

[0029] Real-time analysis and risk warning of users’ health data based on risk assessment models, including:

[0030] By collecting the user's health data and environmental characteristics, a risk assessment model is constructed, wherein the input of the risk assessment model is the current user's health data, and the output is a health risk score;

[0031] Whether to trigger a risk warning is determined based on the health risk score. When the health risk score exceeds the set score threshold, a high-risk warning is triggered. The background data management module sends the risk warning information to the user's mobile terminal and the doctor's mobile terminal, and provides a health intervention plan.

[0032] Optionally, the background data management module includes a security management submodule, which is used to securely protect the user's health data through multi-layer encryption technology and a hierarchical authority mechanism. The hierarchical authority mechanism includes role authority classification, dynamic authorization management and / or data isolation, wherein the role authority classification is to configure different data access and management permissions for users, doctors and administrators respectively, the dynamic authorization management is to dynamically authorize sensitive operations, and the sensitive operations include data export operations and / or permission modification operations. The data isolation includes logical isolation and physical isolation. The logical isolation is to configure a unique identifier for each user to ensure data access isolation, and the physical isolation is to store sensitive information data in the health data separately from ordinary information data.

[0033] Optionally, the user's health data is protected by multiple layers of encryption technology, including:

[0034] Using end-to-end data encryption technology to encrypt the health data at rest, and to independently encrypt the health data of each user;

[0035] Encrypt health data during transmission using secure transmission protocols;

[0036] Use independent encryption keys for different data nodes in the health data to enhance the anti-attack ability;

[0037] Combine symmetric encryption and asymmetric encryption to provide double-layer protection for the health data.

[0038] Optionally, the data collection device includes a smart wearable device and a data entry interface. The smart wearable device at least includes a sphygmomanometer, a blood glucose meter, and a smart bracelet. The data collection device is used to detect abnormal data and give a preliminary warning for the abnormal data.

[0039] Optionally, obtain the user's health data through questionnaire surveys, speech recognition technology, and image processing technology. Among them, obtain the user's health data by processing the pictures uploaded by the user through image processing technology, analyze the voice uploaded by the user through speech recognition technology to obtain the user's health data, and enter the user's health data through the data entry interface.

[0040] Optionally, the user mobile terminal at least includes:

[0041] A health data collection module, which is used to transmit the user's health data to the background data management module;

[0042] A management plan receiving module, which is used to receive the rehabilitation plan in real time and automatically disassemble the rehabilitation plan to generate a user daily task target plan;

[0043] A health task execution and feedback module, which is used to record the completion status of the user daily task target plan, give an automatic prompt when it is detected that the user daily task target plan is not completed, and give a timed reminder according to the user daily task target;

[0044] A health trend display module, which is used to automatically update the user's health data and generate a health data comparison chart according to the user's historical health data set;

[0045] An interactive communication module, which is used to realize real-time interaction between the user and the doctor.

[0046] Optionally, the doctor mobile terminal at least includes,

[0047] A patient management module, which is used to conduct full-course grouping management and dynamic health monitoring of users;

[0048] A management plan generation module, which is used to track and guide the progress of the user's execution of the rehabilitation plan, and dynamically adjust the user's rehabilitation plan according to the evaluation results of the rehabilitation progress;

[0049] A smart data analysis module, which is used to set the priority of user task execution according to the user's health data and transmit the execution result to the user mobile terminal;

[0050] A follow-up management module for generating a follow-up plan according to the user's disease type, disease course stage and rehabilitation needs, the follow-up plan including a follow-up time interval and a follow-up type, wherein the rehabilitation plan is optimized in real time according to the user's follow-up feedback.

[0051] Optionally, the background data management module at least includes

[0052] A management plan distribution sub-module for monitoring the progress of the user's execution of the rehabilitation plan in real time;

[0053] An intelligent analysis sub-module for performing real-time analysis and risk warning based on the user's health data;

[0054] A data storage and security sub-module for storing and encrypting the user's health data.

[0055] Optionally, the background data management module is also used to automatically generate a health assessment report according to the follow-up data submitted by the user and the change trend of the user's health data, the health assessment report including the improvement of the user's symptoms, the prediction of health risks and a comparison chart of the rehabilitation progress, and the health assessment report is sent to the user's mobile terminal and the doctor's mobile terminal in the form of a visual chart.

[0056] Optionally, the background data management module is also used to

[0057] Collect the health data of several users, aggregate the health data with the same disease characteristics into group data, and generate a statistical result after the data aggregation is completed;

[0058] Based on the statistical result of the group data aggregation, automatically screen out high-risk patients, form a priority intervention list, and send the priority intervention list to the doctor's mobile terminal.

[0059] Beneficial effects:

[0060] 1. The present application proposes a closed-loop health management system based on the collaborative work of a user mobile terminal, a doctor mobile terminal and a background data management module. The health management system covers functions such as user health data collection, personalized rehabilitation plan generation, task execution feedback and dynamic optimization, and solves the problems of lack of coordination, intelligence and personalized management in the prior art.

[0061] 2. Through intelligent monitoring, follow-up and task management, the system can timely detect abnormalities in the patient's health status and trigger warnings, reducing the readmission rate and medical complications caused by the deterioration of the disease or non-compliance with medical advice. The personalized health tasks and dynamic optimization mechanism help patients complete rehabilitation management more efficiently and reduce long-term health risks. Description of the Drawings

[0062] The accompanying drawings, which form a part of this application, are used to provide a further understanding of this application, making other features, objectives, and advantages of this application more obvious. The schematic embodiments and descriptions of the accompanying drawings of this application are used to explain this application and do not constitute an improper limitation of this application. In the drawings:

[0063] Figure 1 It is a schematic structural diagram of a whole-course health management system based on artificial intelligence provided by an embodiment of this application;

[0064] Figure 2 It is a schematic structural diagram of a user mobile terminal provided by an embodiment of this application;

[0065] Figure 3 It is a schematic structural diagram of a doctor mobile terminal provided by an embodiment of this application;

[0066] Figure 4 It is a schematic structural diagram of a background data management module provided by an embodiment of this application. Detailed implementation manners

[0067] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the present invention in combination with the accompanying drawings and the descriptions of the embodiments or the prior art. Obviously, the following descriptions of the structures of the accompanying drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings. It should be noted here that the descriptions of these embodiments are used to help understand the present invention, but do not constitute a limitation of the present invention.

[0068] It should be understood that although terms such as first and second may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, the first unit can be called the second unit, and similarly, the second unit can be called the first unit, without departing from the scope of the exemplary embodiments of the present invention.

[0069] Embodiment 1:

[0070] As Figure 1 shown is a schematic structural diagram of a whole-course health management system based on artificial intelligence proposed by an embodiment of the present invention, including:

[0071] A user mobile terminal 10, configured to receive a rehabilitation plan and obtain the health data of the user through a data acquisition device, and transmit the health data of the user to the background data management module in real time.

[0072] Specifically, as Figure 2As shown, the user mobile terminal is the main tool for patients to perform health management tasks. Its core function is to help patients receive the rehabilitation plan (personalized management plan) formulated by doctors, and achieve continuous management of health behaviors through setting task reminders and completion feedback. The design of the user mobile terminal is not only convenient for patients to use, but also realizes the real-time and digitalization of patients' health information through data collection of wearable devices.

[0073] Main features of the user mobile terminal:

[0074] 1. Data collection and synchronization: Support patients to collect health data through wearable devices or manual input, and upload it to the background system (background data management module) in real time.

[0075] 2. Task execution and feedback: Help patients break down the management plan into specific daily tasks (such as water intake target, exercise plan), and improve patients' compliance through timed reminders and task records.

[0076] 3. Health trend display: Display the dynamic changes of patients' health data in the form of charts to help patients intuitively understand their health status.

[0077] 4. Health interaction support: Provide a real-time communication channel between patients and doctors to solve the problems encountered by patients in implementing the plan.

[0078] The doctor mobile terminal 20 is used to obtain the health data of users, analyze the condition of users based on the health data of users, generate a rehabilitation plan when the health data is abnormal, and synchronously transmit the rehabilitation plan to the background data management module and the user mobile terminal.

[0079] Specifically, as Figure 3 shown, the doctor mobile terminal is an important tool for doctors to manage patients' health status and formulate rehabilitation plans. Doctors can view the data uploaded by patients in real time through this mobile terminal, analyze their health status, generate a rehabilitation plan, and push the plan to patients. The doctor mobile terminal aims to reduce the work burden of doctors, improve the diagnosis and treatment efficiency, and support doctors' decision-making through intelligent analysis.

[0080] Main features of the doctor mobile terminal:

[0081] 1. Patient grouping management: Doctors can group and manage patients according to disease types, severity of illness, or stages of diagnosis and treatment, facilitating centralized management of patient groups with different characteristics. 2. Real-time monitoring of health data: Through the back-end data platform, the doctor's APP updates patients' health data in real time, helping doctors quickly understand the current status of patients. 3. Generation of personalized rehabilitation plans: The system provides a management plan template for doctors, and doctors can adjust the content according to the specific situation of patients to generate personalized rehabilitation plans. 4. Dynamic follow-up and optimization: Support doctors to regularly follow up patients, record follow-up results, and adjust subsequent rehabilitation plan programs based on patients' health data.

[0082] The back-end data management module 30 is used to realize data interaction between the user side and the doctor side, and perform real-time analysis and risk warning on the user's health data based on a time series prediction model or / and a risk assessment model.

[0083] Specifically, as Figure 4 shown, the back-end data management module is the core coordination center of the entire full-course health management, responsible for integrating data interaction, intelligent analysis, and task distribution between the user side and the doctor side, ensuring the coordination and closed-loop nature of the management process. By performing real-time analysis on the user's health data, it generates decision-making suggestions for doctors and promotes the execution of health management tasks.

[0084] Main features of the back-end data platform:

[0085] 1. Data storage and cleaning: The back-end data management module cleans and standardizes the data uploaded by the user side to ensure the accuracy and integrity of the data.

[0086] 2. Intelligent analysis and warning: The back-end data management module performs trend analysis and risk prediction on the user's health data, timely discovers potential health risks, and triggers warnings.

[0087] Among them, performing trend analysis and risk prediction on the user's health data realizes the following functions:

[0088] 1) Trend analysis: By analyzing the user's time series health data (such as blood glucose, blood pressure, heart rate, etc.), identify the change trend of health indicators, predict the future indicator changes, and determine whether there are abnormal fluctuations in the data.

[0089] 2) Risk prediction: Combining the user's health history data, current health status, and relevant characteristics, comprehensively calculate the health risk, and generate a health risk assessment result for patients whose health risk score exceeds the set threshold.

[0090] 3) Warning trigger: Based on trend analysis and risk prediction results, when abnormal trends or high risks are detected, health warnings are triggered. Health reminders (such as adjusting lifestyle habits) are sent to the user end, and risk notifications are pushed to the doctor end, suggesting intervention.

[0091] As an embodiment of the present application, real-time analysis and risk warning of the user's health data are performed based on a time series prediction model, including:

[0092] Based on the acquired health data of the user, preprocessing the health data of the user, wherein the preprocessing is to remove abnormal data and fill in missing data;

[0093] Based on the pre-processed user health data, the data fluctuation range of the health data within a preset time period in the future is predicted through the time series prediction model;

[0094] When it is detected that the data fluctuation range of health data in the future preset time period exceeds the preset data fluctuation range, a risk warning is triggered. The background data management module sends the risk warning information to the user's mobile terminal and the doctor's mobile terminal, and provides health advice plans;

[0095] Specifically, 1. Collect the user's health data (such as blood sugar, heart rate, blood pressure) and historical data as the basis for analysis and prediction. 2. Data processing and analysis: pre-process the user's health data, clean up outliers and extract key features. 3. Based on the pre-processed user's health data, predict the data fluctuation range of the health data in the future preset time period through the time series prediction model. 4. When it is detected that the data fluctuation range of the health data in the future preset time period exceeds the preset data fluctuation range, a risk warning is triggered. The background data management module sends the risk warning information to the user's mobile terminal and the doctor's mobile terminal, and provides health advice plans.

[0096] Example: If a continuous upward trend in blood sugar levels is detected and future blood sugar levels are predicted to be outside the safe range, an abnormal blood sugar warning is triggered.

[0097] As an embodiment of the present application, real-time analysis and risk warning of the user's health data are performed based on the risk assessment model, including:

[0098] By collecting the user's health data and environmental characteristics, a risk assessment model is constructed, wherein the input of the risk assessment model is the current user's health data, and the output is a health risk score;

[0099] Whether to trigger a risk warning is determined based on the health risk score. When the health risk score exceeds the set score threshold, a high-risk warning is triggered. The background data management module sends the risk warning information to the user's mobile terminal and the doctor's mobile terminal, and provides a health intervention plan.

[0100] Specifically, by collecting the user's health data and environmental characteristics (such as season, temperature), a risk assessment model is constructed. The input of the risk assessment model is the health data of the current user, and the output is the health risk score. According to the time series trend characteristics (such as slope, fluctuation amplitude) and individual static characteristics (such as age, gender, medical history) obtained from the user's health data, a risk assessment model (such as XGBoost or random forest) is used to classify and predict risks. The model outputs a health risk score (such as the risk value is between 0 and 100) based on the user's health data and environmental characteristics. The model output is the health risk score and level (low risk, medium risk, high risk), which is used to determine whether further intervention is needed. Anomaly detection: Use statistical methods (such as Z-Score) or unsupervised learning algorithms (such as isolation forest) to identify abnormal points in the health data. Early warning trigger logic: When the health risk score exceeds the set threshold, a health warning is triggered.

[0101] Example: If the heart rate data fluctuates violently in a short period of time and is accompanied by a high risk score, a cardiovascular risk warning is triggered. As an embodiment of the present application, the background data management module includes a security management sub-module, and the security management sub-module is used to protect the user's health data through multi-layer encryption technology and hierarchical permission mechanism. The hierarchical permission mechanism includes role permission grading, dynamic authorization management, and / or data isolation.

[0102] Among them, 1. Hierarchical permission mechanism:

[0103] 1) Role permission grading:

[0104] Patients: Can only view and manage their own health data and cannot access the data of others.

[0105] Doctors: Can only access the data of the patients associated with them and cannot view the data of other users.

[0106] Administrators: Can only perform system management and cannot directly access user data.

[0107] 2) Dynamic authorization management:

[0108] For highly sensitive operations (such as data export operations, permission modification operations), dynamic authorization is required. Dynamic authorization is, for example, two-factor authentication (such as SMS, password, etc.).

[0109] 3) Data isolation:

[0110] Logical isolation: Configure a unique identifier for each user, and based on the user's unique identifier, ensure data access isolation.

[0111] Physical isolation: Store sensitive data information (such as contact information) separately from ordinary information data.

[0112] 4) Data access logs:

[0113] All access records are retained, including the operation time, accessed content, and user identity.

[0114] Support regular reviews and detection of abnormal behaviors.

[0115] II. Multi-layer encryption technology:

[0116] This application protects the privacy and data security of patients through multi-layer encryption technology and hierarchical permission mechanisms.

[0117] 1) Storage encryption: Use end-to-end data encryption technology to encrypt the static storage of user health data to ensure that the data cannot be directly read even if it is leaked. Each user's data is encrypted independently to prevent the risk of global data leakage.

[0118] 2) Transmission encryption: Use a secure transmission protocol to encrypt the transmission of data between the user's mobile terminal, the doctor's mobile terminal, and the background data management module. Prevent interception or tampering during transmission.

[0119] 3) Distributed encryption: Different data nodes use independent encryption keys to enhance the anti-attack ability of the distributed system.

[0120] 4) Two-factor encryption: Combine symmetric encryption (efficient data processing) and asymmetric encryption (protecting keys) to achieve double-layer protection.

[0121] This application constructs a closed-loop system for whole-course health management based on multi-terminal collaboration, covering the entire process before, during, and after diagnosis. Through the seamless collaboration of the user mobile terminal (user-side APP), doctor mobile terminal (doctor-side APP), and background data management module (background platform), a closed-loop of task generation, execution, feedback, dynamic optimization, and follow-up management is achieved. The platform can dynamically generate personalized health tasks (such as medication plans, exercise plans, follow-up tasks), and perform intelligent optimization by combining real-time feedback data and the patient's health status. At the same time, it integrates IoT devices to collect the user's health data in real time, identifies abnormalities through the background analysis engine, triggers intelligent warnings, notifies doctors for intervention, and reduces health risks. The system supports automated follow-up and long-term health management. Through functions such as intelligent follow-up plans, online consultations, and health education, it provides comprehensive patient management support for doctors, and generates health assessment reports and personalized diagnosis and treatment suggestions through data analysis. With gamification design and intelligent reminder mechanisms, the platform significantly improves the patient's task completion rate and compliance. The overall technology adopts hierarchical permission management and data encryption to ensure the security of user data. Through multiple technological innovations, this system comprehensively improves the efficiency of medical services, patient experience, and personalized health management capabilities, providing strong technical support for intelligent whole-course health management.

[0122] As an embodiment of this application, the data acquisition device includes intelligent wearable devices and a data entry interface. The intelligent wearable devices at least include a sphygmomanometer, a blood glucose meter, and a smart bracelet. The data acquisition device is used for detecting abnormal data and giving a preliminary warning for the abnormal data.

[0123] Data acquisition device

[0124] The data acquisition device is an important part of the system, mainly including intelligent wearable devices (such as a sphygmomanometer, a blood glucose meter, a smart bracelet) and a data entry interface. The main function of these devices is to realize the real-time collection and upload of multi-dimensional health data.

[0125] Main features of the data acquisition device:

[0126] 1. Diversified data collection: Supports the collection of various types of data, such as physiological indicators (blood pressure, blood glucose, body temperature), behavioral indicators (number of exercise steps, sleep time), and life records (diet, water intake).

[0127] 2. Real-time data transmission: Automatically synchronizes the collected data to the user-side APP through Bluetooth or WiFi connection, and then uploads it to the background data platform.

[0128] 3. Compatibility design: The system supports compatibility with various brands and types of intelligent devices to ensure the flexibility of the acquisition device.

[0129] 4. Abnormal data detection: The device has built-in preliminary data processing capabilities, which can issue early warnings for abnormal data (such as high heart rate) to remind patients to pay attention.

[0130] The full course health management system is realized: 1. Full course coverage: from pre-diagnosis monitoring to post-diagnosis follow-up, covering the patient's entire health management cycle. 2. Real-time data interaction: Real-time data between all ports is interconnected to ensure the accuracy and timeliness of management decisions. 3. Improved feedback mechanism: The user's health data and task completion status are directly fed back to the doctor, and the doctor can dynamically adjust the management plan. Automation and intelligence: The system realizes data analysis and risk prediction, reduces the doctor's repetitive workload, and improves patient compliance.

[0131] As an embodiment of the present application, the user mobile terminal at least includes:

[0132] Health data collection module, used to transmit the user's health data to the background data management module;

[0133] A management plan receiving module is used to receive the rehabilitation plan in real time and automatically disassemble the rehabilitation plan to generate a user's daily task target plan;

[0134] The health task execution and feedback module is used to record the completion of the user's daily task target plan, automatically remind when it is detected that the user's daily task target plan is not completed, and make regular reminders according to the user's daily task target;

[0135] Health trend display module, used to automatically update the user's health data and generate health data comparison charts based on the user's historical health data set;

[0136] Interactive communication module, used to realize real-time interaction between users and doctors.

[0137] Specific: User mobile terminal function overview: The user mobile terminal is the main platform for patients to perform health management tasks, providing functions such as health data collection, management plan reception, health task execution feedback, and interactive support.

[0138] 1. Health data collection module

[0139] Function overview: Responsible for collecting patients' health data and uploading it to the background.

[0140] Secondary functions:

[0141] 1). Automatic collection by smart devices: Collect data (such as blood pressure, heart rate, and number of steps) through wearable devices.

[0142] 2). Manual data entry: Patients can manually enter health data (such as diet records, medication time).

[0143] 3). Historical data query: Support patients to query previously entered data (such as blood pressure records in the past month).

[0144] 4). Abnormal data reminder: When the entered data exceeds the normal range, the system reminds the patient in real time.

[0145] 2. Management plan receiving module:

[0146] Function overview: Display the rehabilitation plan formulated by the doctor to facilitate the patient's execution.

[0147] Secondary functions:

[0148] 1). Plan push notification: Receive the rehabilitation plan pushed by the doctor's mobile terminal in real time.

[0149] 2). Plan decomposition task: Decompose the rehabilitation plan into specific daily tasks (user daily task target plan).

[0150] For example: Example list of daily task target plans: 8:00: Take 500 mg of metformin. 12:00: High-protein lunch recipe (chicken breast + broccoli). 19:00: Walk 6000 steps.

[0151] 3). Plan goal tracking: Real-time display of the progress of plan goal completion (such as water intake, number of exercise steps).

[0152] 4). Update reminder function: When the plan changes, the patient side receives an update notice.

[0153] The system generates adjustment suggestions based on the analysis results: For example: 1. Increase the task goals with high completion rates (such as adjusting the daily steps from 6000 steps to 7000 steps). 2. Decrease the task goals with low completion rates (such as shortening the exercise time from 30 minutes to 20 minutes). 3. Add additional tasks (such as increasing the daily monitoring frequency when blood pressure fluctuates greatly).

[0154] 3. Health task execution and feedback module

[0155] Function overview: Help users complete the daily task target plan and record feedback data.

[0156] Secondary functions:

[0157] 1). Task reminder: Set a timed reminder according to the plan (such as medication time reminder).

[0158] 2). Execution record: Automatically record the user's task completion situation (such as the number of days of exercise compliance).

[0159] 3). Self-evaluation: Users can conduct self-evaluation on the task completion effect (such as "feeling of completion").

[0160] 4). Incomplete Warning: When the task is not completed, the system automatically generates a prompt to remind the patient to complete it.

[0161] 4. Health Trend Display Module

[0162] Function Overview: Visualize health data to help patients understand their own health trends.

[0163] Secondary Functions:

[0164] 1). Real-time Trend Chart: Dynamically update the daily health data changes of users (such as weight curve).

[0165] 2). Historical Comparison Chart: Compare recent and past health data (such as monthly average steps).

[0166] 3). Index Analysis and Suggestions: Generate system health improvement suggestions based on trends.

[0167] 4). Customized Display: Users can choose to display specific indicators (such as only showing blood glucose trends).

[0168] 5. Interactive Communication Module

[0169] Function Overview: Support real-time interaction between patients and doctors.

[0170] Secondary Functions:

[0171] 1). Text Communication: Users send questions to doctors through the message system.

[0172] 2). Picture Upload: Support users to upload pictures of test data or physical abnormalities.

[0173] 3). Task Feedback: Users can actively feedback problems in task execution to doctors.

[0174] 4). Health Education Interaction: Users can ask doctors questions about health education content.

[0175] As an embodiment of the present application, the doctor's mobile terminal at least includes,

[0176] Patient Management Module, used for full-course grouping management and dynamic health monitoring of users;

[0177] Management Plan Generation Module, used for tracking and guiding the progress of users' rehabilitation plans, and dynamically adjusting the rehabilitation plans of patients according to the evaluation results of the rehabilitation progress;

[0178] Intelligent Data Analysis Module, used for setting the priority of user task execution according to the health data of users, and transmitting the execution results to the user's mobile terminal;

[0179] The follow-up management module is used to generate a follow-up plan according to the user's disease type, disease course stage, and rehabilitation needs. The follow-up plan includes the follow-up time interval and the follow-up type. Among them, according to the user's follow-up feedback, the rehabilitation plan is optimized in real time.

[0180] Specifically, the user mobile terminal provides doctors with tools for patient health data analysis, management plan formulation, personalized plan generation, and follow-up management.

[0181] 1. Patient management module

[0182] Function overview: Conduct full-course grouped management and dynamic health monitoring of patients.

[0183] Secondary functions:

[0184] 1). Grouped management: Group patients according to disease type, severity of illness, or management stage.

[0185] 2). Real-time data monitoring: Doctors can view the health data uploaded by patients in real time.

[0186] 3). Health risk classification: The system classifies patients into "high risk", "medium risk", and "low risk" according to the data analysis results.

[0187] 4). Abnormality reminder: When the patient's data is abnormal, the system sends a notification to the doctor.

[0188] 2. Management plan generation module

[0189] Function overview: Develop a comprehensive health management plan for patients.

[0190] Secondary functions:

[0191] 1). Plan template selection: The system provides standardized management and rehabilitation plan templates for doctors to refer to.

[0192] 2). Personalized adjustment: Doctors adjust the content of the rehabilitation plan according to the actual situation of the patient (such as adding diet control).

[0193] 3). Multi-dimensional rehabilitation plan design: Support the formulation of rehabilitation management strategies in multiple dimensions such as diet, exercise, medication, and follow-up.

[0194] 3. Intelligent data analysis module

[0195] Function overview: Decompose the rehabilitation plan into specific plans that users can execute daily.

[0196] Secondary functions:

[0197] 1). Plan task refinement: Refine the rehabilitation plan into daily tasks (such as "reach 5000 steps per day").

[0198] 2). Execution priority setting: Set the task priority according to the user's situation (such as "high priority for medication").

[0199] 3). Quantification of planned goals: Define goals with specific data (such as the daily salt intake not exceeding 5 grams).

[0200] 4). Update synchronization function: Automatically synchronize to the patient side after the plan is updated.

[0201] 4. Intelligent data analysis module

[0202] Function overview: Support doctors to conduct trend analysis and health assessment on patient data.

[0203] Secondary functions:

[0204] 1). Health score calculation: The system generates a health status score for the patient.

[0205] 2). Trend prediction: Predict future health changes based on current data (such as the trend of increasing blood sugar).

[0206] 3). Individualized analysis: Doctors can conduct personalized analysis based on specific user indicators.

[0207] 4). Group data statistics: Aggregate and analyze the grouped health data of users.

[0208] 5. Follow-up management module

[0209] Function overview: Support doctors to arrange and track follow-up tasks for users.

[0210] Secondary functions:

[0211] 1). Follow-up plan formulation: Generate follow-up tasks and arrange follow-up dates.

[0212] 2). Follow-up reminder push: The system pushes follow-up reminders to doctors and users.

[0213] 3). Follow-up result recording: Record the follow-up content and patient feedback.

[0214] 4). Follow-up effect analysis: Analyze the follow-up data and optimize the subsequent follow-up plan.

[0215] As an embodiment of this application, the background data management module at least includes

[0216] The management plan distribution sub-module is used to monitor the progress of users' implementation of the rehabilitation plan in real time;

[0217] The intelligent analysis sub-module is used to conduct real-time analysis and risk warning based on the user's health data;

[0218] Data storage and security sub-module, used for storing and encrypting users' health data.

[0219] Specifically, the background data management module is the core coordination hub of the entire system, supporting management plan generation, data processing, intelligent analysis, and plan distribution.

[0220] 1. Rehabilitation plan support sub-module

[0221] Function overview: Provide intelligent support for doctors to develop rehabilitation plans.

[0222] Secondary functions:

[0223] 1). Plan recommendation function: Generate standardized rehabilitation plan suggestions based on users' health data.

[0224] For example, the rehabilitation plan can cover short-term and long-term goals:

[0225] Short-term goal: Stabilize fasting blood glucose at 4.5 - 6.0 mmol / L within 1 month.

[0226] Long-term goal: Lose 10 kg within 6 months or maintain blood pressure within the normal range.

[0227] Medication plan for the rehabilitation plan:

[0228] Specify the drug, dosage, taking time, and precautions (such as taking before or after meals).

[0229] Exercise plan: Set daily exercise goals (such as steps, calorie consumption), and recommend exercise types (such as jogging, swimming).

[0230] Follow-up visit plan: Arrange follow-up visit times (such as once every 3 months), and adjust according to the patient's condition at any time.

[0231] Nutrition plan: Recommend diet structures (such as high-protein, low-fat diets), and provide executable daily recipes.

[0232] Health guidance plan: Include lifestyle interventions (such as smoking cessation, salt restriction), and provide actionable implementation suggestions.

[0233] Follow-up plan: Set the follow-up frequency (such as once a week), and clarify the follow-up content (such as data upload, symptom feedback).

[0234] Evaluation plan: Determine the evaluation period and indicators (such as evaluate weight, blood glucose, and exercise compliance after 1 month).

[0235] Education plan: Push health education content (such as diabetes management knowledge) to the patient-side APP.

[0236] Temporary Plan: In case of emergencies (such as post-operative rehabilitation), doctors can add planned tasks in real time.

[0237] 2). Data Comparison and Analysis: Optimize the rehabilitation plan through horizontal or vertical data comparison.

[0238] 3). Dynamic Adjustment Suggestions: Provide adjustment suggestions based on the real-time changes in the user's health data.

[0239] 4). Template Update Support: Provide updated plan templates for doctors.

[0240] 2. Management Plan Distribution Sub-module

[0241] Function Overview: Coordinate the generation and distribution of management plans.

[0242] Secondary Functions:

[0243] 1). Plan Task Distribution: Distribute tasks to the user side according to task priorities.

[0244] Priority Classification: Set priorities according to the importance of tasks: High priority: Such as medication, key monitoring (blood sugar, blood pressure). Medium priority: Such as exercise, diet goals. Low priority: Such as reading educational content.

[0245] 2). Status Synchronization Function: Synchronize the task completion status in real time.

[0246] 3). Progress Tracking Function: The background platform can monitor the plan execution progress in real time.

[0247] 4). Exception Handling Function: When the task progress deviates from the plan, the platform reminds the doctor.

[0248] 3. Intelligent Analysis Sub-module

[0249] Function Overview: Provide trend analysis and prediction of health data.

[0250] Secondary Functions:

[0251] 1). Generation of Health Data Trend Graph: Generate graphs showing changes in the user's health indicators.

[0252] 2). Risk Prediction Function: Predict potential health risks based on the model.

[0253] 3). Index Correlation Analysis: Analyze the relationships between multiple indicators (such as blood sugar and weight).

[0254] 4). Group Statistical Analysis: Provide doctors with statistical reports on the health status of patient groups.

[0255] 4. Data Storage and Security Sub-module

[0256] Function Overview: Ensure data storage and privacy security.

[0257] Secondary function:

[0258] 1). Hierarchical storage function: Store data according to the dimensions of patients, doctors, and tasks.

[0259] 2). Data encryption function: Protect data with multiple layers of encryption.

[0260] 3). Access permission management: Different user roles have different permissions.

[0261] 4). Data backup function: Support regular automatic backup of data.

[0262] The whole-course health management system fully considers the dynamics of patient health management, and ensures the scientificity and effectiveness of the health management plan through real-time data monitoring and risk prediction.

[0263] - Technical features:

[0264] 1. Before diagnosis: Collect users' health data through intelligent devices and generate health assessment reports.

[0265] 2. During diagnosis: Support doctors to formulate personalized management plans (rehabilitation plans) based on the analysis results and optimize the diagnosis and treatment process.

[0266] 3. After diagnosis: Ensure the realization of users' long-term health goals through continuous follow-up and dynamic management.

[0267] - Advantages:

[0268] - Solve the problems of fragmented functions and data islands in the existing technology.

[0269] - Provide patients with a full-process management service throughout the health life cycle.

[0270] Example 2:

[0271] I. The management plan (rehabilitation plan) of this application covers multi-dimensional contents such as users' diet, exercise, medication, follow-up, etc., and is dynamically adjusted according to users' real-time data and task completion status.

[0272] Technical features:

[0273] 1. Dynamic optimization: When users execute the rehabilitation plan, the system collects feedback data in real time and analyzes the task completion status to help doctors optimize subsequent plans.

[0274] 2. Refined management: Through the task refinement function, the management plan is split into specific daily tasks (such as walking 6,000 steps and drinking 2,000 ml of water per day) to improve patients' compliance.

[0275] Advantages: It breaks the "one-size-fits-all" model in traditional health management and achieves a high degree of personalization of management plans. Through the dynamic adjustment function, the adaptability of the management plan and the long-term implementation effect of patients are improved.

[0276] II. Efficient Doctor-Patient Interaction Mechanism

[0277] 1. Real-time messaging system: Users can consult doctors about health problems through the user-side APP, and doctors can give professional replies through the doctor-side APP.

[0278] 2. Data-driven interaction: After users complete tasks or upload health data, the doctor-side APP updates relevant data in real time, supporting doctors to make accurate judgments.

[0279] 3. Personalized reminder: The system automatically reminds users to complete the plan according to the task execution situation of users, or reminds doctors to pay attention to abnormal users.

[0280] 4. Traceable record: All interaction contents are recorded by the system, and doctors and users can view historical communication records at any time.

[0281] Advantages: It improves the efficiency and accuracy of doctor-patient interaction. Through two-way interaction, it strengthens patients' sense of participation and compliance with the health management plan.

[0282] III. Closed-loop Feedback Task Execution Monitoring

[0283] 1. Real-time completion monitoring: Through intelligent devices or manual records by users, the system monitors the task completion situation (such as the daily exercise goal achievement rate). 2. Data visualization: Intuitive charts are used to display the task completion situation and progress, such as the trend chart of medication compliance rate. 3. Abnormal handling mechanism: When the task execution deviates from the expectation (such as the task not being completed for multiple days), the system automatically reminds the user and notifies the doctor.

[0284] Advantages: It improves the execution rate of users' health behaviors and the task completion rate. The closed-loop feedback mechanism enables doctors to quickly adjust the management plan to ensure the achievement of health goals.

[0285] IV. Data Security and Privacy Protection

[0286] 1. Data encryption: End-to-end encryption is used during the transmission process, and multi-layer encryption technology is used during the storage process. 2. Permission management: Different user roles have different data access permissions to ensure the legality of data use. 3. Anonymization processing: Personal identities are anonymized in group data analysis to avoid privacy leakage. 4. Data backup and recovery: The system regularly backs up data to support disaster recovery.

[0287] Advantages: It protects users' privacy and enhances users' trust in the system. The high security of data enables the technology to be widely applied in medical scenarios.

[0288] A detailed description is given below in combination with specific application scenarios:

[0289] The system realizes multi-dimensional, real-time and accurate health data collection through intelligent devices, user manual input, online questionnaires and dynamic monitoring technologies, and uploads it to the background data platform for processing and analysis. The following are the detailed expansions and steps:

[0290] I. Data collection scope and dimensions:

[0291] 1. Physiological data: 1) Physiological indicators such as heart rate, blood pressure, blood sugar, body temperature, blood oxygen saturation, weight, BMI, etc. 2) Dynamically monitor electrocardiogram (ECG) and sleep quality.

[0292] 2. Behavioral data: 1) Daily steps, exercise duration, exercise intensity, calorie consumption. 2) Monitor sedentary time (sedentary behavior).

[0293] 3. Lifestyle data: 1) Diet structure (daily calorie and nutrient intake). 2) Water intake, daily routine (wake-up and bedtime).

[0294] 4. Symptoms and subjective feelings: 1) Symptoms subjectively described by users (such as dizziness, fatigue, pain), which are collected regularly through the user-side APP.

[0295] II. Data collection methods

[0296] Multiple data collection methods are adopted to ensure comprehensiveness, real-time and data quality:

[0297] 1. Automatic collection by intelligent devices: The system supports the connection of mainstream intelligent devices (such as blood glucose meters, blood pressure monitors, smart bracelets, electrocardiogram monitors), and obtains data in real time. The device synchronizes the data to the user-side APP through Bluetooth, WiFi or cellular network. The data is automatically stamped with a timestamp and the collection device identifier to ensure the accuracy and traceability of the data source.

[0298] 2. Manual data entry: Users can manually enter health data that cannot be collected by intelligent devices (such as emotional state, diet records) through the user-side APP. The system provides an intelligent input interface, such as automatically matching the food name and calories entered in the diet database.

[0299] 3. Online health questionnaire: Users enter subjective feelings and symptoms (such as pain score, sleep quality) through regular health questionnaires. The questionnaire design is dynamically adjusted to add detailed questions for specific health risks (such as diabetes).

[0300] 4. Dynamic Behavior Monitoring: Continuously monitor the user's motion status (such as walking, running, cycling) through smart devices and automatically identify behavior patterns. The system detects abnormal behaviors (such as sitting still for a long time, sudden interruption of exercise) in real time and triggers reminders.

[0301] 5. Scheduled Data Collection Plan: The system sets the data collection frequency for specific patients (such as high-risk patients measuring blood pressure every 6 hours), and sends reminders through the APP to ensure the timeliness of data.

[0302] III. Data Upload Process

[0303] The collected health data is uploaded through the following process:

[0304] 1. Initial Data Processing: The user-side APP preprocesses the collected data (such as removing obvious outliers) and formats the data. The patient ID, collection timestamp, and device identifier are added when the data is packaged to ensure data uniqueness.

[0305] 2. Encrypted Upload: End-to-end data encryption technology (such as AES-256) is adopted to ensure the security of data during transmission. The data is uploaded to the backend data platform through secure communication protocols (such as HTTPS, MQTT).

[0306] 3. Resume Upload Mechanism: In case of unstable network or device power-off, the system supports resuming data upload from the breakpoint to ensure complete data upload. After the upload is completed, the system sends a confirmation notice to the user-side APP, indicating successful data synchronization.

[0307] 4. Real-time Synchronization and Delay Tolerance: For critical physiological data (such as electrocardiogram, blood pressure, etc.), the system supports real-time upload mode, and the data is synchronized to the backend platform immediately after collection. For non-critical data (such as diet records), it supports scheduled batch upload mode to reduce device power consumption and communication pressure.

[0308] IV. Data Verification and Storage

[0309] The uploaded data is verified and stored by the backend data management module to ensure data quality and availability:

[0310] 1. Data Verification: Format Verification: Check whether the data meets the upload format requirements (such as consistent units, reasonable numerical range). Integrity Verification: Verify whether the uploaded data packet is complete without missing fields or content. Logical Verification: Check the logical relationship between data (such as "steps" cannot be negative, and the systolic blood pressure should be higher than the diastolic blood pressure).

[0311] 2. Data Storage: Hierarchical Storage: The backend system stores data in different database partitions according to data types (physiological data, behavioral data, and life data). Time-Series Storage: Time-sensitive data (such as heart rate and blood pressure) is stored in a time-series database to support efficient trend analysis. Backup and Recovery: Patient data is automatically backed up daily to support disaster recovery.

[0312] 3. Data Annotation: The system adds classification annotations to data (such as "abnormal" and "high priority") to support subsequent analysis. In special cases (such as too high heart rate), the data will trigger a risk marker and enter the warning queue.

[0313] To improve the integrity, accuracy, and consistency of data, the system introduces the following data quality management mechanisms:

[0314] 1. User Guidance: The APP integrates guiding instructions for data collection operations (such as the correct posture for blood pressure measurement and precautions for blood glucose collection). Warnings and correction suggestions for incorrect data entry are provided.

[0315] 2. Self-Calibration of Collection Devices: Supports calibration protocols with collection device manufacturers, and updates device calibration parameters through the backend to reduce data deviation.

[0316] 3. Multi-Source Data Comparison: Collects the same type of data from multiple devices (such as heart rate data from a smart bracelet and a sphygmomanometer) for comparison to verify data consistency.

[0317] 4. Intelligent Completion Mechanism: For data not entered by the patient in a timely manner (such as missed dietary reports), the system intelligently completes it based on historical behavior patterns and known data and marks it as an "estimated value".

[0318] V. Handling and Reminding of Abnormal Data

[0319] The system processes and immediately reminds of the collected abnormal data to avoid health management deviations caused by data anomalies.

[0320] 1. Abnormal Value Detection: The system detects anomalies in data based on the normal value range of the patient (individually set). If the blood pressure is too high or the heart rate fluctuates abnormally, the system automatically marks it as an "abnormal value". 2. Abnormal Data Confirmation Mechanism: For the detected abnormal values, the system requests the patient to re-measure and upload. The doctor-side APP can manually confirm or correct the abnormal data. 3. Abnormal Reminder: If the abnormal data exceeds the set threshold (such as blood glucose > 20 mmol / L), the system automatically sends a reminder: Patient Reminder: Reminds the patient to check the data, re-measure, or contact the doctor. Doctor Reminder: Pushes an emergency notice to the doctor-side APP and attaches the patient's recent data.

[0321] VI. Health Data Analysis and Risk Assessment

[0322] Health data analysis and risk assessment are the core aspects of this patented technology. The system deeply analyzes multi-dimensional health data uploaded by patients to generate health status assessments, trend predictions, and risk warnings. The following are the specific functions, technical implementations, and detailed execution steps of this part:

[0323] 1. Data preprocessing and organization

[0324] Before data analysis, the background data platform comprehensively cleans, standardizes, and integrates the collected data to ensure data quality and the accuracy of analysis results.

[0325] Format standardization: Convert data from different sources (such as manually entered and uploaded by smart devices) into a unified format. Ensure that data units are consistent (e.g., blood glucose in mmol / L and body weight in kg).

[0326] 2. Outlier detection and handling: The system screens for outliers through rule-based detection methods (such as fixed normal ranges) and machine learning-based detection methods (such as clustering analysis).

[0327] The following processing is performed on the detected outliers:

[0328] Minor anomalies: Marked as "suspicious values" and retained.

[0329] Severe anomalies: The system requests the patient to recollect or manually confirm.

[0330] 3. Missing value imputation: Use methods such as time series interpolation, mean filling, or historical data inference to impute missing data. Mark the source of the imputed data (such as "estimated value") to distinguish it from real data.

[0331] 4. Time alignment and synchronization: Align data uploaded by multiple devices according to timestamps to ensure the time consistency of the analyzed data. For example, heart rate and step count data are aligned at the minute level to judge physiological changes after exercise.

[0332] VII. Health status assessment

[0333] The system uses real-time health data and historical data uploaded by patients to comprehensively analyze the patient's current health status and provide quantitative health assessment results for doctors and patients.

[0334] Execution steps:

[0335] 1. Multi-dimensional health scoring: Calculate a comprehensive health score (such as 0 - 100 points) based on the patient's key health indicators (such as blood pressure, heart rate, blood glucose, body weight). Each indicator is assigned a weight and adjusted individually in combination with the patient's medical history.

[0336] 2. Health Indicator Comparison: The system compares the patient's current data with the healthy reference range to generate a status assessment for each indicator (such as "normal", "high", "abnormal"). Provide the comparison results of the patient's current indicators with the historical average.

[0337] 3. Personalized Status Classification: Classify the patient's health status according to the disease characteristics or health goals of the patient (such as "stable period", "mild abnormality", "high risk"). Provide a detailed description of the classification basis (such as "recent blood pressure has been continuously high").

[0338] 4. Health Advice Generation: The system generates targeted health advice based on the assessment results, such as "it is recommended to increase daily exercise amount" and "regularly measure blood sugar and upload it".

[0339] VIII. Health Trend Analysis

[0340] Through a time series prediction model, analyze the change trend of the user's health data (the data fluctuation range of health data within a preset future time period), and discover potential health risks or abnormal developments.

[0341] Execution Steps:

[0342] 1. Data Trend Modeling:

[0343] The system uses time series analysis algorithms (such as ARIMA model, LSTM network) to model the patient's continuous data (such as blood pressure fluctuations, weight changes). Predict the change range of health data in the future for a period of time (such as the blood sugar fluctuation range in the next 7 days).

[0344] 2. Historical Trend Review:

[0345] The system displays the change curves of health indicators in the past period of time (such as 1 week, 1 month, 3 months). Combining with the user's health goals, evaluate whether the indicators are developing in the ideal direction.

[0346] 3. Multidimensional Data Association Analysis:

[0347] The system analyzes the association relationships between different health data (such as the association between high-salt diet and increased blood pressure). Provide an analysis of possible factors affecting health changes (such as insufficient recent exercise leading to weight gain).

[0348] 4. Dynamic Change Reminder:

[0349] Send dynamic reminders to patients with abnormal trend changes (such as "blood pressure has fluctuated greatly in the last week, it is recommended to increase the monitoring frequency"). Push the trend change report to the doctor's end.

[0350] IX. Risk Assessment and Early Warning

[0351] Based on the analysis results of health data, real-time warnings are issued to high-risk patients.

[0352] Execution steps:

[0353] 1. Risk assessment model construction: The system constructs a risk prediction model (such as a logistic regression model or a deep neural network) based on the user's medical history data, real-time data, and environmental characteristics.

[0354] The evaluation objectives include: the risk of disease onset (such as the risk of acute onset of cardiovascular disease), and the risk of complications (such as the risk of nephropathy in diabetic patients).

[0355] 2. Risk probability calculation: Calculate the health risk score through the risk assessment model and compare it with the set score threshold.

[0356] 3. Risk classification and priority ranking: The system classifies the user's multiple health risks (such as low, medium, high). High-priority risks are automatically pushed to the doctor's end (such as "the risk of cardiovascular disease onset in a certain patient > 80%").

[0357] 4. Real-time warning and notification: Patient-side reminder: When a risk is detected, the patient-side APP sends a reminder notification and advises the user to contact the doctor. Doctor-side notification: Push a detailed risk assessment report to the doctor-side APP, including risk categories, probabilities, and data support (such as "the patient's blood pressure fluctuates greatly, increasing the risk of cardiovascular disease").

[0358] X. Group data analysis and support

[0359] To support doctors in better managing the user's patient group, the system statistically analyzes the group data to identify common health problems.

[0360] Execution steps: 1. Group data aggregation: The system aggregates data of the same disease or specific characteristics (such as patients with hypertension). Provide group statistical results such as average indicators and the proportion of abnormal data.

[0361] 2. Group health trend analysis: Analyze the changes in the health trends of a specific patient group (such as the blood sugar change trend of diabetic patients in a certain department). Provide group-based health management suggestions (such as "increase diet management education").

[0362] 3. Screening of high-risk patients: The system screens out high-risk patients from the group data to form a priority intervention list. Push the high-risk list to the doctor's end for the doctor to manage centrally.

[0363] 4. Generation of group health reports: Generate group health analysis reports for doctors or medical institutions to refer to for formulating broader health management strategies.

[0364] Example 3:

[0365] This application realizes the formulation, pushing, execution, and feedback management of patients' health tasks. Through an intelligent task engine and real-time data interaction function, it ensures that health tasks are effectively executed.

[0366] 1. Health task generation

[0367] 1) Task types:

[0368] Medication plan: Generate personalized medication reminders (including drug name, dosage, frequency, time, etc.). Exercise plan: Develop an exercise plan based on the patient's physical condition (such as daily step goal, exercise duration, etc.). Diet plan: Provide dietary suggestions that meet the patient's health needs. Follow-up reminder: Set a regular follow-up plan for the patient. Follow-up plan: Generate questionnaire follow-up tasks to track the patient's recovery progress. Health education: Push health knowledge content (articles, videos) related to the patient.

[0369] 2) Generation basis:

[0370] Tasks are formulated by the doctor side according to the patient's health data (such as physical signs, disease diagnosis) and medical guidelines. The background intelligent algorithm automatically generates based on the patient file and historical execution data.

[0371] 2. Task pushing

[0372] I. 1). APP push reminder: Send clear task calendar events through the user-side APP, and patients can view the daily task list on the task interface. 2) SMS reminder: For users who have not opened the APP, the system can send task reminders via SMS. 3) Intelligent voice reminder: Announce the daily health tasks to the patient through the voice assistant function.

[0373] II. Customized push frequency: The doctor side can set the push frequency and time nodes of tasks (such as daily, weekly) to avoid repeated tasks from interfering with the user's daily life.

[0374] 3. Task execution management:

[0375] The user-side APP provides a task module, and patients can view the to-do task list on the interface. For each task, the user can mark the completion status ("completed", "partially completed", "not completed"). For tasks not completed on time, the system will send a reminder notice or prompt for manual intervention on the doctor side. The user can view the task completion progress chart in real time and obtain a reward mechanism (such as points, badges, etc.) to improve the execution enthusiasm.

[0376] 4. Task feedback and data synchronization

[0377] After completing the task, the user can fill in the actual feelings and problems encountered during the task execution (such as medication side effects, discomfort, etc.) through the APP. The system supports uploading pictures or voice descriptions (such as uploading photos of taking medicine).

[0378] Doctor side reception and analysis: All task execution data (completion rate, feedback, etc.) of patients will be synchronized to the doctor side APP, and doctors can quickly view the execution status of patients. For the problems mentioned in the feedback, doctors can immediately adjust the task or communicate with patients through the APP.

[0379] Analyze the execution effect of health tasks based on the task completion rate and patient feedback, and provide data support for subsequent task adjustments. The system will generate a personalized health report for patients, showing the execution rate, the changing trend of health status, warning indicators, etc.

[0380] 5. Dynamic optimization mechanism

[0381] Automatically optimize tasks: The platform automatically optimizes task content based on feedback data and analysis results. For example, when users feedback that the exercise plan is too heavy, the system will adjust the plan intensity. The system dynamically adjusts key parameters such as medication reminder frequency or exercise duration according to changes in health indicators.

[0382] The doctor side APP will receive the analysis reports generated by the system (such as patient health risk assessment, feedback summary), and doctors can manually adjust the patient's health management plan according to the reports. Doctors and users can communicate in real time through the APP (such as online health consultation) to jointly explore more suitable management methods.

[0383] 6. Follow-up and long-term management

[0384] The follow-up and long-term management module is an important part of the combination of the platform and the APP, aiming to provide systematic and continuous health tracking services for patients. Through the collaborative work of the user side, doctor side and the platform, real-time monitoring of the disease state, continuous evaluation of the rehabilitation effect and dynamic adjustment of the health management plan are realized, so as to ensure the long-term health status of patients, reduce the recurrence risk and improve the quality of life.

[0385] 6.1 Follow-up plan formulation: Personalized follow-up plan: According to the patient's disease type, disease course stage and rehabilitation needs, the doctor side generates a personalized follow-up plan. The platform combines the patient's health data and AI algorithms to automatically recommend the best follow-up time interval and frequency (such as postoperative follow-up, chronic disease management follow-up). Support formulating differentiated follow-up paths for different patient groups (postoperative patients, chronic disease patients, high-risk groups).

[0386] 6.2 Follow-up Types: Online Questionnaire Follow-up: Push questionnaires to regularly obtain patients' symptom feedback and treatment effects. Phone Follow-up: Automatically assign follow-up tasks to doctors or nurses through the platform and record communication content. Video Follow-up: Conduct remote video consultations through the user-side APP to understand patients' conditions in real time. Offline Follow-up: Remind patients to come to the hospital for reexamination and synchronize the patients' arrival at the hospital to the doctor side.

[0387] 6.3 Follow-up Reminders and Execution: The platform pushes follow-up reminders through the APP, text messages, or voice to ensure that patients participate in follow-up on time. The doctor-side APP can view the progress of follow-up tasks completed. For patients who fail to complete on time, the system will automatically generate re-reminder tasks.

[0388] 6.4 Patient Participation: The user-side APP displays a detailed follow-up task list, including follow-up time, precautions, etc. Patients can submit follow-up questionnaires, upload test data, or directly schedule video follow-ups through the APP. If patients ignore follow-up tasks, the system will increase the reminder frequency and record the uncompleted situation for the doctor side to refer to.

[0389] Telemedicine Support: Support remote video consultations between the user side and the doctor side, and synchronously save the follow-up results and consultation records to the background system. The system uses speech recognition and natural language processing (NLP) technologies to record and analyze remote consultation content.

[0390] This application can achieve:

[0391] 1. Improve the efficiency of medical services and resource utilization: Through modular design and distributed architecture, the system has high scalability and reliability, supports massive user access and multi-task concurrent processing. Asynchronous communication technology and efficient data management mechanisms reduce system response latency and ensure stable operation of the platform under high load. Combining dynamic optimization and task priority scheduling algorithms reduces doctors' workload in task generation and follow-up management, and improves the allocation efficiency of medical resources.

[0392] 2. Follow-up and task management functions, through intelligent scheduling and full-process tracking, help doctors quickly grasp patients' health conditions, generate intuitive follow-up reports and health trend charts, and support doctors' accurate decision-making. The platform's health risk scoring model further optimizes the priority of medical resource use, reduces waste of medical resources, and improves the health intervention effect of patients.

[0393] 3. Achieve personalized and closed-loop health management: The platform constructs a patient-centered closed-loop management system through the complete process of health task generation, execution, feedback, optimization, and follow-up. Combining AI algorithms and medical guidelines, the system generates scientific and personalized health tasks for users and dynamically adjusts task content, frequency, and reminder methods according to users' behaviors and feedback.

[0394] For example, through the intelligent analysis of patient feedback, the system can quickly identify problem points and optimize tasks in real time, effectively improving patient compliance. At the same time, the automated follow-up plan and task adjustment are dynamically optimized according to the patient's health status and changes in the condition, ensuring the accuracy of management.

[0395] 4. Enhance doctor-patient interaction and data-driven diagnosis and treatment: The two-way data flow between the user side and the doctor side enables doctors to obtain the patient's health data, task execution status, and follow-up feedback in real time, thus supporting personalized diagnosis and treatment decisions. The health assessment reports, condition trend charts, and risk warning information automatically generated by the system reduce the doctor's processing time for raw data and improve the diagnosis and treatment efficiency.

[0396] The platform's health task completion status and follow-up data also provide rich decision-making support for doctors. For example, through functions such as video consultations, health education, and intelligent reminders, doctors and patients can communicate and interact efficiently, achieving a closer doctor-patient relationship and more efficient health management services.

[0397] 5. Improve patient participation and self-management ability: The platform significantly improves patient participation through the design of the health task module and interactive functions. For example, through gamification designs such as task points and completion badges, users are more willing to actively participate in health management. In addition, the user-side APP provides a clear task calendar and multiple task completion methods (such as voice, picture, and text submission), reducing the usage difficulty and increasing the patient's task completion rate.

[0398] The health education module helps patients better understand their own diseases and rehabilitation methods by dynamically pushing personalized health education content (such as health knowledge videos, articles) and behavior intervention suggestions, thus enhancing the patient's self-management ability and health awareness.

[0399] 6. Reduce medical costs and health risks: Through intelligent monitoring, follow-up, and task management, the platform can promptly detect abnormalities in the patient's health status and trigger warnings, reducing the readmission rate and medical complications caused by disease deterioration or non-compliance with doctor's orders. The personalized health tasks and dynamic optimization mechanism help patients complete rehabilitation management more efficiently, reducing long-term health risks.

[0400] In addition, the long-term health management services provided by the platform for chronic disease patients (such as medication reminders, lifestyle adjustment suggestions) significantly reduce the acute incidence rate and treatment costs of high-risk patients, saving overall medical expenses.

[0401] 7. Ensure data security and user trust: The system adopts international mainstream standards (such as GDPR, HIPAA) in user privacy protection, and ensures the security and compliance of user data through AES-256 data encryption and hierarchical permission management. The platform's transparent data management process and user data deletion authorization function enhance user trust and avoid potential legal compliance risks at the same time.

[0402] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

[0403] The above are only the preferred embodiments of the present invention, and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An artificial intelligence-based full-course health management system, characterized in that, include: The user mobile terminal is used to receive the rehabilitation plan and obtain the user's health data through the data acquisition device, and transmit the user's health data to the background data management module in real time; The doctor's mobile terminal is used to obtain the user's health data, analyze the user's condition based on the user's health data, generate a rehabilitation plan when the health data is abnormal, and synchronously transmit the rehabilitation plan to the background data management module and the user's mobile terminal; The backend data management module is used to realize data interaction between the user end and the doctor end, and to perform real-time analysis and risk warning of the user's health data based on the time series prediction model and / or risk assessment model; Among them, real-time analysis and risk warning of users' health data are carried out based on the time series prediction model, including: Based on the acquired health data of the user, preprocessing the health data of the user, wherein the preprocessing is to remove abnormal data and fill in missing data; Based on the pre-processed user health data, the data fluctuation range of the health data within a preset time period in the future is predicted through the time series prediction model; When it is detected that the data fluctuation range of health data in the future preset time period exceeds the preset data fluctuation range, a risk warning is triggered. The background data management module sends the risk warning information to the user's mobile terminal and the doctor's mobile terminal, and provides health advice plans; Real-time analysis and risk warning of users’ health data based on risk assessment models, including: By collecting the user's health data and environmental characteristics, a risk assessment model is constructed, wherein the input of the risk assessment model is the current user's health data, and the output is a health risk score; Whether to trigger a risk warning is determined based on the health risk score. When the health risk score exceeds the set score threshold, a high-risk warning is triggered. The background data management module sends the risk warning information to the user's mobile terminal and the doctor's mobile terminal, and provides a health intervention plan.

2. The system according to claim 1, characterized in that The background data management module includes a security management submodule, which is used to securely protect the user's health data through multi-layer encryption technology and a hierarchical authority mechanism. The hierarchical authority mechanism includes role authority classification, dynamic authorization management and / or data isolation, wherein the role authority classification is to configure different data access and management permissions for users, doctors and administrators respectively, the dynamic authorization management is to dynamically authorize sensitive operations, and the sensitive operations include data export operations and / or permission modification operations. The data isolation includes logical isolation and physical isolation. The logical isolation is to configure a unique identifier for each user to ensure data access isolation, and the physical isolation is to store sensitive information data in the health data separately from ordinary information data.

3. The system according to claim 2, characterized in that The user's health data is protected through multi-layer encryption technology, including: Using end-to-end data encryption technology to encrypt the health data at rest, and to independently encrypt the health data of each user; Encrypt health data during transmission using secure transmission protocols; Use independent encryption keys for different data nodes in the health data to enhance the anti-attack ability; Combine symmetric encryption and asymmetric encryption to provide double-layer protection for the health data.

4. The system according to claim 1, wherein The data acquisition device includes a smart wearable device and a data entry interface. The smart wearable device at least includes a sphygmomanometer, a blood glucose meter, and a smart bracelet. The data acquisition device is used for detecting abnormal data and giving preliminary warnings for the abnormal data.

5. The system according to claim 4, wherein Obtain the user's health data through questionnaire surveys, speech recognition technology, and image processing technology. Among them, process the pictures uploaded by the user through image processing technology to obtain the user's health data, analyze the voices uploaded by the user through speech recognition technology to obtain the user's health data, and enter the user's health data through the data entry interface.

6. The system according to claim 1, wherein The user mobile terminal at least includes: A health data acquisition module for transmitting the user's health data to the background data management module; A management plan receiving module for receiving the rehabilitation plan in real time and automatically disassembling the rehabilitation plan to generate a user daily task target plan; A health task execution and feedback module for recording the completion status of the user daily task target plan, automatically prompting when it detects that the user daily task target plan is not completed, and giving timed reminders according to the user daily task target; A health trend display module for automatically updating the user's health data and generating a health data comparison chart based on the user's historical health data set; An interactive communication module for realizing real-time interaction between the user and the doctor.

7. The system according to claim 1, characterized in that, The doctor mobile terminal at least includes A patient management module for grouping and managing the user throughout the course of the disease and dynamically monitoring the health; A management plan generation module for tracking and guiding the progress of the user's execution of the rehabilitation plan, and dynamically adjusting the user's rehabilitation plan according to the evaluation results of the rehabilitation progress; An intelligent data analysis module for setting the priority of the user task execution according to the user's health data and transmitting the execution result to the user mobile terminal; A follow-up management module for generating a follow-up plan according to the user's disease type, disease course stage, and rehabilitation needs. The follow-up plan includes the follow-up time interval and the follow-up type. Among them, according to the user's follow-up feedback, the rehabilitation plan is optimized in real time.

8. The system according to claim 1, characterized in that, The background data management module at least includes A management plan distribution sub-module for monitoring the progress of the user's execution of the rehabilitation plan in real time; An intelligent analysis sub-module for performing real-time analysis and risk warning according to the user's health data; A data storage and security sub-module for storing and encrypting the user's health data.

9. The system according to claim 1, wherein The background data management module is also used for automatically generating a health assessment report according to the follow-up data submitted by the user and the change trend of the user's health data. The health assessment report includes the improvement of the user's symptoms, health risk prediction, and a rehabilitation progress comparison chart. The health assessment report is sent to the user mobile terminal and the doctor mobile terminal in the form of a visual chart.

10. The system according to claim 1, characterized in that, The background data management module is also used for Collecting the health data of several users, aggregating the health data with the same disease characteristics, and generating a statistical result after the data aggregation is completed; Based on the aggregated statistical results of population data, automatically screen out high-risk patients, form a priority intervention list, and send the priority intervention list to the doctor's mobile terminal.

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