Intelligent health management system

Abstract

The invention provides an intelligent health management system. The intelligent health management system comprises a data acquisition module, a data transmission module, a data processing module, a health assessment module and a health intervention module. The data acquisition module acquires physiological, behavior and environmental data of a user; the data transmission module uploads data in real time through wireless communication; the data processing module adopts a deep learning algorithm to analyze the health data, calculates a health risk score and generates an evaluation result; the health assessment module provides a health report; the health intervention module generates a personalized health regimen based on the evaluation result, including exercise and drug reminders. The system supports time sequence prediction, can early warn health risks in advance, is linked with a medical institution, and provides professional medical suggestions and long-term health management support for users. According to the invention, multi-dimensional health data acquisition, intelligent analysis and personalized intervention are realized, the system is suitable for scenes of personal health management, chronic disease monitoring, community health service and the like, and the real-time performance and accuracy of health management are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of health management systems, and particularly to an intelligent health management system. Background Art

[0002] With the development of modern society and the improvement of people's health awareness, health management has become an important part of personal life and social development. Traditional health management methods mainly rely on regular physical examinations and manual recording of health data. This method has problems such as discontinuous data collection, single monitoring content, information lag, and lack of personalized management, and cannot meet the growing health management needs. At the same time, the increasing prominence of health problems such as chronic diseases and sub-health, as well as the accelerating arrival of an aging society, have put forward higher requirements for the intelligence, diversification, and real-time of health management systems.

[0003] Current situation of the prior art: Most current health management systems rely on a single device (such as a smart bracelet or smart watch) or a physical examination institution for data collection, and the types of health data that can be provided are limited. Common collection contents include a small number of health indicators such as heart rate, steps, and blood oxygen saturation.

[0004] Analysis of deficiencies: Limited collection scope: Health data is limited to physiological indicators (such as heart rate and blood oxygen), while behavioral data (such as sleep quality and exercise amount) and environmental data (such as temperature and humidity) are not fully integrated, resulting in an insufficiently comprehensive overall assessment of the user's health.

[0005] Lack of continuity: The physical examination data of medical institutions is usually once a year, and the interval of health data is relatively long, making it difficult to reflect the dynamic changes of the health status. The data collection frequency of daily health monitoring devices is relatively high, but the data is isolated and scattered, and cannot form a long-term continuous health trajectory.

[0006] Existing systems mostly process health data based on simple statistical methods or rule algorithms. For example, the heart rate is judged abnormal according to a set threshold, or the activity level is evaluated by accumulating the number of steps.

[0007] Difficulty in capturing complex health characteristics: Health data usually has multi-dimensional and time-series characteristics, and relying on simple rule algorithms cannot fully explore the complex relationships in the data. For example, the health status of hypertensive patients may be affected by multiple factors (such as heart rate, environmental temperature, and exercise amount) together, and the analysis of a single indicator may lead to incorrect judgments.

[0008] The algorithms of existing systems lack consideration of individual differences and mostly use fixed thresholds or universal rules, resulting in generalization of health management recommendations. For example, the adaptability of different age groups to exercise intensity varies significantly, but the system cannot dynamically adjust the exercise target according to the individual's historical data.

[0009] The current technology mainly analyzes static health data and lacks the ability of time series prediction. It is unable to predict the user's future health status or potential risks, making it difficult to prevent the occurrence of health problems in a timely manner.

[0010] Some intelligent health devices have the function of real-time data collection, but the efficiency of data transmission and processing is relatively low, and users cannot obtain feedback quickly. The application of health data by medical institutions still mainly relies on offline data, with less online data linkage and remote services.

[0011] There may be a long delay in the process of data transmission from the collection device to the processing center. Especially when the data needs to be uploaded to the cloud for processing, it is difficult for users to obtain timely feedback on their health status. For example, the delay in abnormal heart rate monitoring may lead to high-risk events not being warned in a timely manner.

[0012] There is a lack of an effective linkage mechanism between the health management system and medical institutions. It is difficult for users' health data to be quickly transmitted to doctors, and doctors have limited understanding of users' long-term health conditions, making it difficult to provide personalized diagnosis and treatment suggestions for users.

[0013] Currently, most of the intervention suggestions provided by health management systems are relatively general. For example, it is recommended to increase physical activity or control diet, but no customized solutions are provided according to the specific health status and living habits of users.

[0014] The suggestions of the health management system are mostly based on universal rules. For example, it is recommended to walk 8,000 steps every day. Such suggestions ignore the actual health status, exercise ability, and target needs of users and lack pertinence for special populations (such as the elderly and chronic disease patients).

[0015] The system lacks the evaluation of the effects after users execute intervention measures and cannot dynamically adjust the intervention plan according to the changes in users' behaviors and health status, resulting in insufficient continuity and precision of health management.

[0016] Health data has a high degree of privacy, but there are still obvious deficiencies in data encryption, access control, and privacy protection in existing systems.

[0017] Risk of data leakage: During the process of data collection, transmission, and storage, due to the lack of high-strength encryption technology, the data may be stolen or illegally used by hackers.

[0018] Lack of privacy control: Users have weak control over their health data and cannot effectively limit the scope of data use or know whether the data has been misused by third parties, which easily leads to users' concerns about privacy leakage.

[0019] The role of the intelligent health management system in assisting medical resource allocation has not been fully exploited. For example, the system has difficulty in identifying the priorities and urgencies of health management and cannot classify and manage chronic disease patients and healthy populations.

[0020] Health data has not been effectively analyzed and classified, resulting in some patients with minor illnesses seeking medical treatment frequently, increasing the burden on medical institutions, while high-risk patients who truly need urgent intervention may not be identified in a timely manner.

[0021] Lack of long-term health management support:

[0022] The health management system usually only focuses on the short-term health status of users and fails to provide systematic and long-term health management support for chronic disease patients, especially lacking in aspects such as postoperative rehabilitation and chronic disease monitoring.

[0023] In summary, the existing technologies have many deficiencies in data collection, intelligent analysis, real-time performance, personalized intervention, privacy protection, and medical resource linkage. These problems limit the effectiveness and coverage of the intelligent health management system in practical applications.

[0024] Therefore, we urgently need to design an intelligent health management system to solve the above problems. Summary of the Invention

[0025] The purpose of the present invention is to provide an intelligent health management system in view of the deficiencies of the existing technologies to solve the problems raised in the background technology.

[0026] To achieve the above purpose, the present invention provides the following technical solution: An intelligent health management system includes a data collection module, a data transmission module, a data processing module, a health assessment module, and a health intervention module. The data collection module is used to collect multi-dimensional health data X = {x1, x2,..., x n}, where x i represents the i-th health parameter, and n represents the total number of health parameters;

[0027] The data transmission module transmits the collected data to the data processing module in real time through wireless communication technology;

[0028] The data processing module analyzes the data through artificial intelligence algorithms to generate health assessment indicators Y = f(X, W), where f(·) represents a deep learning model and W is the model parameter matrix;

[0029] The health assessment module generates a user health status report;

[0030] The health intervention module provides personalized health management suggestions based on the assessment results.

[0031] As a preferred technical solution of the present invention, the data acquisition module includes multiple sensors, which respectively collect heart rate HR, blood oxygen saturation SpO2, body temperature Temp, exercise amount Act, and environmental temperature EnvTemp, and form a feature vector:

[0032] X = [HR, SpO2, Temp, Act, EnvTemp],

[0033] where X represents the set of collected health parameters.

[0034] As a preferred technical solution of the present invention, the data processing module calculates the user's health risk score Risk through the following formula:

[0035]

[0036] where: σ(·) is the activation function, used to map data to the interval [0, 1]; w i is the weight of each health parameter; x i is the i-th health parameter; b is the bias value; Risk represents the user's health risk score, and the higher the value, the greater the risk.

[0037] As a preferred technical solution of the present invention, the activation function σ(·) is the Sigmoid function, defined as:

[0038]

[0039] where represents the weighted linear combination result of the health parameters.

[0040] As a preferred technical solution of the present invention, the health status evaluation result generated by the health evaluation module includes a health score HealthScore, and the calculation formula is:

[0041] HealthScore = α·(1 - Risk) + β·Act,

[0042] where: α and β are weight factors, satisfying α + β = 1; HealthScore represents the user's comprehensive health score; 1 - Risk represents the reverse index of health risk; Act represents the user's exercise amount.

[0043] As a preferred technical solution of the present invention, the health intervention module provides an intervention suggestion I based on the user's health score HealthScore and risk score Risk, where:

[0044]

[0045] where I represents the health intervention suggestion.

[0046] As a preferred technical solution of the present invention, the data processing module optimizes the model parameter W through a deep neural network to minimize the following loss function:

[0047]

[0048] where: m is the number of samples; y j is the actual health score of the jth sample; f(X j , W) is the health score predicted by the model; is the mean squared error loss function.

[0049] As a preferred technical solution of the present invention, the optimization algorithm of the deep neural network is Stochastic Gradient Descent (SGD), and its parameter update rule is:

[0050]

[0051] where: η is the learning rate; is the gradient of the loss function with respect to the parameter W.

[0052] As a preferred technical solution of the present invention, the health assessment module predicts the future health status, and the prediction formula is:

[0053] X t+1 = g(X t , ΔX),

[0054] where: X t represents the health parameters at the current moment; ΔX = X t - X t-1 , represents the change rate of health parameters; g(·) is a time series prediction function.

[0055] As a preferred technical solution of the present invention, the prediction function g(·) is implemented based on a Long Short-Term Memory (LSTM) network, and its output result is used to adjust the health intervention strategy I, and the optimization formula is:

[0056] I t+1 = h(X t+1 ),

[0057] where h(·) is an intervention strategy generation function.

[0058] Compared with the prior art, the beneficial effects of the present invention are:

[0059] The present invention can collect users' physiological data (such as heart rate, blood oxygen, body temperature), behavioral data (such as exercise volume, sleep quality) and environmental data (such as temperature, humidity) through multi-dimensional sensors, realizing comprehensive coverage and dynamic monitoring of health data. At the same time, using real-time data transmission technology to ensure the continuity and timeliness of the collected data, providing a reliable basis for the accurate assessment and real-time intervention of the health status.

[0060] The system combines deep learning algorithms and time series prediction models to intelligently process health data, and can identify complex health characteristics and potential risks. Through the personalized health assessment and intervention plan generation function, the system can dynamically adjust suggestions according to the user's historical data, health goals and current status, such as optimizing exercise plans, diet suggestions and medication reminders, effectively improving the accuracy of health management and the user experience.

[0061] The system has a real-time early warning function for abnormal health status, and can timely detect health problems and send reminders through an efficient health risk assessment model. The data sharing and linkage support function with medical institutions can provide auxiliary diagnostic basis for doctors on the user's long-term health data, helping users obtain professional medical services and continuous health management support, thus significantly improving the prevention and control efficiency of health problems and the utilization effect of medical resources. Brief Description of the Drawings

[0062] Figure 1 It is a system block diagram of an intelligent health management system proposed by the present invention. Detailed Embodiments

[0063] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0064] The following combines Figure 1 several embodiments to describe the detailed embodiments of the present invention in detail.

[0065] Embodiment 1: Basic Health Monitoring and Risk Assessment;

[0066] Function Description: This embodiment is mainly used to realize the collection, real-time transmission, risk analysis and generation of health reports of basic health data.

[0067] Implementation Steps: The user wears an intelligent bracelet, which is built-in with a heart rate sensor, a blood oxygen sensor, a temperature sensor and an acceleration sensor.

[0068] The environmental sensor (standalone device) is installed in the user's home to collect environmental temperature and humidity data.

[0069] The data acquisition module obtains the user's health parameters through the sensor, including heart rate (HR), blood oxygen (SpO2), body temperature (Temp), exercise volume (Act), and environmental temperature (EnvTemp). The data is collected every 1 minute to form the following feature vector:

[0070] X = [HR, SpO2, Temp, Act, EnvTemp].

[0071] Data transmission: Use the Bluetooth protocol to transmit the data from the bracelet to the mobile phone APP, and at the same time synchronize the data of the home environmental sensor to the cloud server through WiFi.

[0072] Data processing and risk calculation The cloud server receives the data and calls the deep learning model to calculate the health risk score Risk:

[0073]

[0074] where the weight w i and the bias b have been obtained through training with a large amount of historical health data.

[0075] Health report generation: The health assessment module generates a report, including trend charts of parameters such as heart rate and blood oxygen, the current health status (such as "normal" or "potential risk"), and the risk score.

[0076] Example 2: Generation of personalized health intervention plan

[0077] Function description: Based on the health risk assessment, this example combines the user's exercise habits and historical health data to generate personalized health management suggestions.

[0078] Calculation of comprehensive health score: User information entry The user fills in personal basic information (age, gender, height, weight, etc.) and health history (such as diabetes, cardiovascular disease history) in the APP.

[0079] The system records the user's exercise volume data (Act) in the past week.

[0080] The system calculates the health score HealthScore according to the following formula:

[0081] HealthScore = α·(1 - Risk) + β·Act,

[0082] The weight factors are α = 0.7 and β = 0.3. Among them, Risk comes from the calculation result of Example 1, and Act is the exercise volume parameter.

[0083] Based on the user's health score and risk score, the health intervention module generates suggestions:

[0084]

[0085] For example, when Risk = 0.8 and Act = 0.2, the system suggests that the user "walk 30 more minutes every day"; when Risk = 0.4 and HealthScore = 0.5, the system suggests "reduce carbohydrate intake and control calories".

[0086] User feedback and adjustment: The user can choose to accept or adjust the suggestions. If the user implements the suggestions, the system will monitor the data changes in real time and dynamically optimize the intervention plan.

[0087] Example 3: Long-term health status prediction and dynamic monitoring;

[0088] Function description: This example predicts the user's future health status based on historical data and time series models, realizing dynamic monitoring and early intervention of the health status.

[0089] Data collection and storage: The data collection module collects the user's health parameter X every day t , and stores the data of the last 30 days {X t-30 , …, X t}.

[0090] Calculation of data change rate: The system calculates the change rate of the health parameter every day:

[0091] ΔX t = X t - X t-1 .

[0092] Prediction of future health status: Use the prediction function g(·) based on the Long Short-Term Memory (LSTM) network to predict the health parameters for the next 7 days:

[0093] X t+1 = g(X t , ΔX t ).

[0094] Early warning of health risks: The prediction results include the possible change trends of future heart rate, blood oxygen and exercise volume.

[0095] If the prediction results show that Risk ≥ 0.7 or HealthScore ≤ 0.05, the system immediately pushes health warning information and contacts the user's health manager or medical institution.

[0096] Adjust the intervention strategy: Dynamically adjust the intervention plan based on the prediction results. For example: increase the user's exercise volume target; remind the user to have a medical examination; push a diet control plan.

[0097] Example 4: Linkage service of medical institutions;

[0098] Function description: This example provides comprehensive health management and medical support through data sharing and linkage with medical institutions.

[0099] Data sharing: After the user authorizes, the system shares their health data (such as heart rate, blood oxygen, body temperature trend) to the health management platform of medical institutions.

[0100] Doctor evaluation and feedback: Based on the health report and prediction data provided by the system, the doctor gives professional opinions, such as "It is recommended to further check the blood sugar level" or "It is recommended to increase the intensity of daily activities".

[0101] Real-time consultation and support: The user directly conducts video consultations with the doctor through the APP to answer health questions.

[0102] The system provides the doctor with the patient's historical data and prediction results to support the doctor in formulating personalized treatment or management plans.

[0103] Implementation of the health plan: Medical institutions adjust their intervention plans according to the user's health conditions, such as prescribing medications or suggesting specific rehabilitation training.

[0104] The content not described in detail in this specification belongs to the prior art well-known to those skilled in the art. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An intelligent health management system, comprising a data collection module, a data transmission module, a data processing module, a health assessment module, and a health intervention module, characterized in that: The data acquisition module is used to collect multi-dimensional health data X = {x1, x2, …, x n}, where x i represents the i-th health parameter, and n represents the total number of health parameters; The data transmission module transmits the collected data to the data processing module in real time through wireless communication technology; The data processing module analyzes the data through artificial intelligence algorithms to generate health assessment indicators Y = f(X, W), where f(·) represents a deep learning model and W is the model parameter matrix; The health assessment module generates a user health status report; The health intervention module provides personalized health management suggestions based on the assessment results.

2. The intelligent health management system according to claim 1, wherein: The data collection module includes multiple sensors that respectively collect heart rate HR, blood oxygen saturation SpO2, body temperature Temp, exercise amount Act, and ambient temperature EnvTemp, forming a feature vector: X = [HR, SpO2, Temp, Act, EnvTemp], where X represents the set of collected health parameters.

3. The intelligent health management system according to claim 1, characterized in that: The data processing module calculates the user's health risk score Risk through the following formula: Where: σ(·) is the activation function, which is used to map data to the interval [0, 1]; w i is the weight of each health parameter; x i is the i-th health parameter; b is the bias value; Risk represents the user's health risk score, and the higher the value, the greater the risk.

4. The intelligent health management system according to claim 3, wherein: The activation function σ(·) is the Sigmoid function, defined as: Among them represents the weighted linear combination result of health parameters.

5. The intelligent health management system according to claim 1, wherein: The health status assessment result generated by the health assessment module includes a health score HealthScore, and the calculation formula is: HealthScore = α·(1 - Risk) + β·Act, where: α and β are weight factors, satisfying α + β = 1; HealthScore represents the user's comprehensive health score; 1 - Risk represents the reverse indicator of health risk; Act represents the user's exercise amount.

6. The intelligent health management system according to claim 1, wherein: The health intervention module provides an intervention suggestion I based on the user's health score HealthScore and risk score Risk, where: where I represents the health intervention suggestion.

7. The intelligent health management system according to claim 1, characterized in that: The data processing module optimizes the model parameter W through a deep neural network to minimize the following loss function: where: m is the number of samples; y j is the actual health score of the j-th sample; f(X j , W) is the health score predicted by the model; L is the mean squared error loss function.

8. The intelligent health management system according to claim 1, wherein: : The optimization algorithm of the deep neural network is Stochastic Gradient Descent (SGD), and its parameter update rule is: where: η is the learning rate; is the gradient of the loss function with respect to the parameter W.

9. The intelligent health management system according to claim 8, characterized in that: The health assessment module predicts the future health status, and the prediction formula is: X t+1 = g(X t , ΔX), Where: X t represents the health parameter at the current moment; ΔX = X t - X t-1 , representing the change rate of the health parameter; g(·) is the time series prediction function.

10. The intelligent health management system according to claim 9, characterized in that: The prediction function g(·) is implemented based on a Long Short-Term Memory (LSTM) network, and its output result is used to adjust the health intervention strategy I. The optimization formula is: I t+1 = h(X t+1 ), where h(·) is the intervention strategy generation function.

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