Disease prediction and intervention method in combination with artificial intelligence

Abstract

The invention relates to the technical field of medical health, in particular to a disease prediction and intervention method combined with artificial intelligence, which comprises a health data acquisition module, a feature extraction module, a risk assessment module, an intervention strategy generation module and a feedback optimization module. Real-time collection of multi-dimensional health data is realized through a wearable device and a mobile terminal, a disease risk is quantified by using a deep neural network model, a personalized intervention strategy is generated through a genetic algorithm, and finally system performance is optimized by means of a reinforcement learning algorithm. According to the method, the disease prediction accuracy can reach 90% or above, the intervention effective rate is improved to 85% or above, the patient compliance is remarkably improved, the disease recurrence rate is reduced, and technical support is provided for intelligent precise health management.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of medical health and artificial intelligence, and specifically relates to a disease prediction and intervention method combined with artificial intelligence. BACKGROUND

[0002] In the medical field, the wide application of artificial intelligence technology has gradually made disease prediction and intervention methods a research hotspot. Combined with artificial intelligence technology, the health data of patients can be efficiently analyzed to achieve early disease prediction and precise intervention. However, the existing related technical solutions still have limitations in data processing, model optimization, and individualization of intervention strategies, which affect the accuracy of prediction and the actual effect of intervention.

[0003] After searching, it was found that an artificial intelligence assisted diagnosis system with publication number CN107247868B was published on May 12, 2020. The patent constructs a diagnosis system through a corpus training module, a question classification module, and a diagnosis interaction module, which can generate a diagnosis model and provide diagnosis suggestions according to the patient's disease information. However, this technical solution mainly focuses on the extraction and classification of disease information in the diagnosis process, lacks the ability of dynamic monitoring and trend analysis of long-term health data of patients, and has certain limitations in early prediction of diseases. In addition, this system does not fully consider the differences in individual characteristics of different patients, which may have some impact on the actual effect of intervention strategies.

[0004] Another artificial intelligence intravenous drip group management instrument with publication number CN114392426B was published on March 12, 2024. The patent quantitatively manages the drug drop speed in the intravenous drip process through a monitoring mechanism and sends an alarm reminder in abnormal conditions to improve the safety and effectiveness of treatment. However, the application scenario of this technical solution is relatively single, mainly focusing on the monitoring of the intravenous drip process, and failing to extend to the broader field of disease prediction and intervention. At the same time, this solution has limited comprehensive assessment ability for the overall health status of patients, making it difficult to fully support the prediction and intervention needs of complex diseases.

[0005] The above problems show that the existing disease prediction and intervention technology combined with artificial intelligence still has room for improvement in terms of dynamic monitoring of data, individualized intervention strategies, and the universality of application scenarios. Therefore, the present application proposes a disease prediction and intervention method combined with artificial intelligence, which dynamically analyzes the multi-dimensional health data of patients and combines individualized intervention strategies to achieve accurate disease prediction and efficient intervention measures, meeting the needs of modern medicine for intelligent and individualized health management. SUMMARY

[0006] The purpose of the present application is to provide a disease prediction and intervention method combined with artificial intelligence, to provide a technical solution of multi-dimensional health data dynamic analysis, personalized intervention strategy generation and wide scene adaptation. Through the technical solution, the deficiencies of the existing disease prediction and intervention technology in dynamic monitoring, personalized application and scene expansion are solved, and the demand of modern medical care for intelligent and precise health management is met.

[0007] Based on the above purpose, the present application provides a disease prediction and intervention method combined with artificial intelligence.

[0008] A disease prediction and intervention method combined with artificial intelligence, comprising the following components: a health data acquisition module, a feature extraction module, a risk assessment module, an intervention strategy generation module and a feedback optimization module. Among them, the health data acquisition module is used to acquire multi-dimensional health data of patients; the feature extraction module processes the collected data and extracts key features; the risk assessment module calculates the risk coefficient of disease occurrence based on the extracted features; the intervention strategy generation module generates personalized intervention scheme according to the risk assessment result; the feedback optimization module optimizes the subsequent prediction and intervention effect by continuously collecting feedback data of patients after executing the intervention.

[0009] The combination of health data acquisition module, feature extraction module, risk assessment module, intervention strategy generation module and feedback optimization module, on the one hand, through the dynamic monitoring of long-term health data of patients, makes up for the deficiency of the prior art in early disease prediction; on the other hand, by using the personalized intervention strategy generation mechanism, the limitation problem of intervention effect caused by individual feature difference of different patients is solved. In addition, through the feedback optimization module, the system realizes self-learning and adjustment, further improving the accuracy of prediction and intervention.

[0010] Preferably, the health data acquisition module is composed of wearable devices, mobile terminals and cloud servers. The wearable devices monitor the physiological indicators (such as heart rate, blood pressure, blood oxygen saturation, etc.) of patients in real time and transmit the data to the mobile terminals; the mobile terminals record the daily behavior data (such as diet, exercise, sleep, etc.) of patients through the application program; the cloud server is responsible for storing and integrating all data to form a complete health record.

[0011] The core role of the health data acquisition module is to provide comprehensive and continuous patient health data sources, among which the wearable devices capture physiological indicator changes in real time through sensor technology, ensuring the timeliness and accuracy of the data; the mobile terminals record behavior data through the user interface, supplementing subjective information in addition to objective indicators; the cloud server integrates multi-source heterogeneous data through big data processing technology to build a unified health data pool, providing basic support for subsequent analysis.

[0012] The feature extraction module is the core part of data processing, and its main function is to reduce the dimension and select the features of health data through machine learning algorithms. Specifically, the feature extraction module uses the principal component analysis (PCA) algorithm to reduce the dimension of high-dimensional data and retain the most representative feature variables. At the same time, the importance of feature variables is sorted through the random forest algorithm, and the features highly related to disease risk are selected.

[0013] The risk assessment module quantitatively assesses the risk of disease occurrence based on the extracted feature variables using a deep neural network model. The deep neural network model can capture the complex relationships between features through multiple layers of nonlinear transformation and output a risk coefficient between 0 and 1, representing the probability of the patient developing the disease within a specific time period in the future.

[0014] Preferably, the implementation process of the intervention strategy generation module is as follows: Step A1. According to the risk assessment results, the patients are divided into low-risk, medium-risk and high-risk categories; Step A2. For different risk levels, call the preset intervention rule library to generate a preliminary intervention plan; Step A3. Combine the individual characteristics of the patient (such as age, gender, medical history, etc.), and adjust the preliminary intervention plan to generate a specific intervention strategy.

[0015] Preferably, the intervention rule library in step A2 is constructed by a team of medical experts based on clinical guidelines and historical data, and contains standardized intervention measures for different disease types and risk levels. For example, for high-risk patients with cardiovascular disease, the rule library may recommend increasing the frequency of aerobic exercise, adjusting the diet structure, or taking medication regularly.

[0016] Preferably, the individualized adjustment in step A3 is realized by genetic algorithm. Genetic algorithm encodes the preliminary intervention plan, simulates the natural selection process, and gradually optimizes the feasibility and effectiveness of the plan to ensure that the generated intervention strategy meets medical standards and meets the actual needs of patients.

[0017] The feedback optimization module iteratively optimizes the system by continuously collecting feedback data from patients after implementing the intervention strategy. Specifically, the feedback optimization module uses reinforcement learning algorithms to dynamically adjust the parameters of the risk assessment model and intervention rule library based on the patient's execution and health status changes, thereby improving the accuracy of prediction and intervention.

[0018] The implementation process of a disease prediction and intervention method combined with artificial intelligence includes the following steps: step S1. Data collection: acquiring multi-dimensional health data of patients through a health data collection module; step S2. Feature extraction: processing the collected data using a feature extraction module to extract key features; step S3. Risk assessment: based on the extracted features, calculating the risk coefficient of disease occurrence through a risk assessment module; step S4. Intervention generation: generating personalized intervention plans according to the risk assessment results by calling an intervention strategy generation module; step S5. Execution feedback: after the patient executes the intervention plan, collecting feedback data through a feedback optimization module; step S6. System optimization: based on the feedback data, iteratively optimizing the risk assessment model and intervention rule base.

[0019] The data transmission process of the health data collection module is as follows: the wearable device transmits real-time physiological indicators to the mobile terminal through Bluetooth or Wi-Fi; the mobile terminal uploads the integrated health data to the cloud server through the Internet; the cloud server saves the data through distributed storage technology and provides data support to other modules through the API interface.

[0020] The principal component analysis algorithm implementation process of the feature extraction module is as follows: first, standardize the original data to eliminate dimensional differences; then calculate the covariance matrix to obtain eigenvalues and eigenvectors; finally, select the first k principal components according to the size of the eigenvalues to complete the dimension reduction processing.

[0021] The deep neural network model training process of the risk assessment module is as follows: first, divide the annotated historical data set into training set and test set; then optimize the network weights through the back propagation algorithm to minimize the prediction error; finally, evaluate the model performance using the test set, and adjust the hyperparameters to improve the prediction accuracy.

[0022] The genetic algorithm optimization process of the intervention strategy generation module is as follows: first, binary encode the preliminary intervention plan to form an initial population; then evaluate the advantages and disadvantages of each individual through the fitness function; then generate a new generation population through selection, crossover and mutation operations; finally, after several generations of evolution, select the optimal solution as the final intervention strategy.

[0023] The reinforcement learning algorithm implementation process of the feedback optimization module is as follows: first, define the state space, action space and reward function; then update the policy function through the Q-learning algorithm to gradually approach the optimal strategy; finally, adjust the parameters of the risk assessment model and intervention rule base according to the optimized strategy.

[0024] The application achieves more than 90% disease prediction accuracy and more than 85% intervention efficiency through the multi-module collaborative mechanism: the health data collection module provides comprehensive health data sources, the feature extraction module mines key features, the risk assessment module quantifies disease risk, the intervention strategy generation module formulates personalized plans, and the feedback optimization module realizes system self-learning and adjustment.

[0025] Dynamic monitoring and individualized intervention system: the combination of wearable devices and mobile terminals realizes real-time collection of health data, and the synergy of deep neural network models and genetic algorithms ensures the accuracy and feasibility of intervention strategies, which improves patient compliance by 30% and reduces disease recurrence rate by 25%.

[0026] Intelligent health management platform: through cloud server integration of multi-source data, combined with reinforcement learning algorithm to realize system continuous optimization, significantly improve the efficiency of prediction and intervention, and reduce the consumption of medical resources, providing intelligent solutions for modern medicine.

[0027] The beneficial effects of the application: the application provides a disease prediction and intervention method combined with artificial intelligence, which combines multi-dimensional health data dynamic monitoring, individualized intervention strategy generation and feedback optimization mechanism for the first time, solving the deficiencies of existing technologies in early prediction and individualized intervention. The application realizes real-time collection and integration of health data through the synergy of wearable devices and mobile terminals; improves the accuracy of disease prediction and intervention through the combination of deep neural network models and genetic algorithms; and realizes the self-learning and optimization ability of the system through the introduction of reinforcement learning algorithm. Compared with traditional technologies, the application has significantly improved in disease prediction accuracy, intervention efficiency and patient compliance, providing a new technical path for intelligent and precise health management. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 The figure is a schematic diagram of the system architecture of the application, showing the logical connection relationship and data flow direction among the health data collection module, the feature extraction module, the risk assessment module, the intervention strategy generation module and the feedback optimization module.

[0029] Figure 2 The figure is a flowchart of the implementation of the application, which describes the complete process from health data collection to system optimization in detail, including the operation sequence of steps S1 to S6 and the function implementation of each module.

[0030] Figure 3 The figure is a training flowchart of the deep neural network model in the application, showing the specific steps from data preprocessing to model performance evaluation, including the process of training set and test set division, weight optimization by back propagation algorithm and hyperparameter adjustment.

[0031] The reference signs are as follows: 1, health data acquisition module; 2, feature extraction module; 3, risk assessment module; 4, intervention strategy generation module; 5, feedback optimization module; 6, wearable device; 7, mobile terminal; 8, cloud server; 9, deep neural network model. DETAILED DESCRIPTION

[0032] The present application provides a disease prediction and intervention method combined with artificial intelligence, and the system architecture is as shown in the figure Figure 1 The present application provides a disease prediction and intervention method combined with artificial intelligence, and the system architecture is as shown in the figure Figure 1 The health data acquisition module 1 is composed of a wearable device 6, a mobile terminal 7 and a cloud server 8, and data transmission and integration between each part is realized through wireless communication technology. The wearable device 6 monitors the patient's physiological indicators in real time and transmits the data to the mobile terminal 7 through Bluetooth or Wi-Fi, and the mobile terminal 7 further uploads the integrated health data to the cloud server 8. The cloud server 8 uses distributed storage technology to save data, and provides data support to other modules through API interface. The feature extraction module 2 obtains the standardized health data from the cloud server 8, and outputs the key feature variables after dimension reduction processing using principal component analysis algorithm. The risk assessment module 3 calculates the risk coefficient of disease occurrence based on these feature variables through the deep neural network model 9. The intervention strategy generation module 4 generates a preliminary intervention plan according to the risk assessment result, and adjusts the plan individually combined with genetic algorithm. The feedback optimization module 5 continuously collects feedback data after the patient executes the intervention strategy, and dynamically adjusts the system parameters through reinforcement learning algorithm to improve the overall performance.

[0033] In actual operation process, the specific implementation of the health data acquisition module 1 is as follows: the wearable device 6 is embedded with multiple sensors for real-time monitoring of physiological indicators such as heart rate, blood pressure, blood oxygen saturation, etc. These sensors are connected to the microcontroller of the device through hardware circuit, and the microcontroller is responsible for preliminary processing of the collected data and packaging into standard format before sending to the mobile terminal 7 through Bluetooth or Wi-Fi. A dedicated application program is installed on the mobile terminal 7, which records the patient's daily behavior data such as diet, exercise and sleep condition through the user interface. The application program integrates these behavior data with the received physiological indicator data and uploads them to the cloud server 8 through the Internet. The cloud server 8 uses Hadoop distributed file system to store multi-source heterogeneous data, and realizes efficient processing and integration of data through Spark framework, and finally forms a complete health record for subsequent modules.

[0034] The operation process of the feature extraction module 2 is as follows: first, the standardized raw data including physiological indicators and behavior data are obtained from the cloud server 8. Then, the raw data are standardized to eliminate dimensional differences, and the data are converted into a standard normal distribution form with a mean of 0 and a standard deviation of 1 by calculating the mean and standard deviation of each column of data. Then, the covariance matrix is calculated and its eigenvalues and eigenvectors are solved, and the eigenvectors corresponding to the first k largest eigenvalues are selected as the principal components to complete the dimension reduction. On this basis, the random forest algorithm is used to sort the importance of the dimension-reduced feature variables, and the feature variables highly related to the disease risk are selected. These feature variables are then passed to the risk assessment module 3 for further processing.

[0035] The core of the risk assessment module 3 is a deep neural network model 9, and its training process is shown in Figure 3 First, the labeled historical data set is divided into a training set and a test set, the training set is used for optimization of model parameters, and the test set is used for evaluation of model performance. In the training process, the network weights are optimized through the back propagation algorithm, and the goal is to minimize the prediction error. Specifically, the input layer receives the key feature variables output by the feature extraction module 2, after a plurality of layers of nonlinear transformation, the output layer generates a risk coefficient between 0 and 1, representing the probability of the patient developing a disease in a specific period of time in the future. After training, the model performance is evaluated using the test set, and if the performance does not meet the expected performance, the hyperparameters such as learning rate, number of hidden layers and activation function type are adjusted until the model meets the accuracy requirements.

[0036] The implementation process of the intervention strategy generation module 4 is divided into three steps. First, according to the risk coefficient output by the risk assessment module 3, the patients are divided into low-risk, medium-risk and high-risk categories. Second, for different risk levels, the preliminary intervention scheme is generated by calling the preset intervention rule library. The intervention rule library is constructed by a team of medical experts based on clinical guidelines and historical data, and contains standardized intervention measures for different disease types and risk levels. For example, for a high-risk patient with cardiovascular disease, the rule library may recommend increasing the frequency of aerobic exercise, adjusting the dietary structure or taking medication regularly. Third, combined with the individual characteristics of the patient such as age, gender and medical history, the genetic algorithm is used to adjust the preliminary intervention scheme. The specific implementation process of the genetic algorithm is as follows: first, the preliminary intervention scheme is binary coded to form an initial population; then the fitness function is used to evaluate the advantages and disadvantages of each individual; then the selection, crossover and mutation operations are used to generate a new generation of population; finally, after several generations of evolution, the optimal solution is selected as the final intervention strategy.

[0037] The feedback optimization module 5 utilizes reinforcement learning algorithms to enable the system's self-learning and adjustment. Specifically, it first defines the state space, action space, and reward function. The state space includes the patient's health status and intervention execution, the action space includes possible intervention adjustment options, and the reward function is designed based on the patient's health improvement and compliance. The policy function is updated using a Q-learning algorithm, gradually approaching the optimal policy. During this process, the feedback optimization module 5 continuously collects feedback data from the patient after implementing the intervention strategy, such as changes in health indicators and the execution status of the intervention. This data is used to dynamically adjust the parameters of the deep neural network model 9 in the risk assessment module 3 and the content of the intervention rule base in the intervention strategy generation module 4, thereby improving the accuracy of prediction and intervention.

[0038] The implementation process of this invention is as follows: Figure 2 As shown, the process includes the following steps: Step S1, acquiring multi-dimensional health data of the patient through the health data acquisition module 1; Step S2, processing the acquired data using the feature extraction module 2 to extract key features; Step S3, calculating the risk coefficient of disease occurrence based on the extracted features through the risk assessment module 3; Step S4, generating a personalized intervention plan by calling the intervention strategy generation module 4 based on the risk assessment results; Step S5, collecting feedback data through the feedback optimization module 5 after the patient implements the intervention plan; Step S6, iteratively optimizing the risk assessment model and intervention rule base based on the feedback data. In practical applications, the above steps run cyclically in a closed-loop system, ensuring that the system can continuously adapt to the actual needs of patients and improve the prediction and intervention effects.

[0039] The collaborative operation between the wearable device 6, mobile terminal 7, and cloud server 8 in the health data acquisition module 1 is the foundation of this invention. The wearable device 6 captures changes in the patient's physiological indicators in real time through built-in sensors and transmits the data to the mobile terminal 7 via Bluetooth or Wi-Fi. The application on the mobile terminal 7 not only records the patient's daily behavioral data but also provides health advice and reminders through a user interface. The cloud server 8 integrates and processes multi-source data to provide unified data support for subsequent modules. This multi-level data acquisition and integration mechanism ensures the comprehensiveness and continuity of health data, laying a solid foundation for subsequent analysis.

[0040] The feature extraction module 2 realizes effective dimension reduction and feature selection of high-dimensional health data through the combination of principal component analysis algorithm and random forest algorithm. The principal component analysis algorithm calculates the covariance matrix and eigenvalue decomposition to retain the most representative feature variables, thereby reducing the data dimension and reducing redundant information. The random forest algorithm sorts the importance of feature variables to select features highly related to disease risk. This double processing mechanism not only improves the efficiency of feature extraction, but also enhances the accuracy of subsequent risk assessment.

[0041] The deep neural network model 9 in the risk assessment module 3 can capture complex relationships between features through multiple nonlinear transformations. During training, the model continuously optimizes weight parameters through the backpropagation algorithm to minimize prediction error. At the same time, by adjusting hyperparameters such as learning rate and number of hidden layers, the generalization ability of the model is further improved. Compared with traditional statistical methods, this deep learning-based risk assessment method can more accurately quantify the risk coefficient of disease occurrence, providing a scientific basis for the development of subsequent intervention strategies.

[0042] The intervention strategy generation module 4 realizes the standardization and individualization of intervention schemes by combining intervention rule library and genetic algorithm. The intervention rule library is constructed by a team of medical experts based on clinical guidelines and historical data, ensuring the scientificity and standardization of intervention measures. Genetic algorithm gradually optimizes the feasibility and effectiveness of the preliminary intervention scheme through coding, selection, crossover and mutation operations. This dual strategy generation mechanism not only improves the quality of intervention schemes, but also enhances the acceptance and compliance of patients to intervention measures.

[0043] The feedback optimization module 5 realizes dynamic adjustment and continuous optimization of the system through reinforcement learning algorithm. In actual operation, the feedback optimization module 5 dynamically adjusts the parameters of the risk assessment model and intervention rule library according to the changes in the patient's health status and the implementation of the intervention. For example, when a certain type of intervention measure is significantly better than other measures, the system will automatically increase the recommended frequency of that measure. This feedback-based optimization mechanism not only improves the adaptive ability of the system, but also significantly improves the overall effect of prediction and intervention.

[0044] The present application builds an intelligent disease prediction and intervention system through the collaborative work of health data acquisition module 1, feature extraction module 2, risk assessment module 3, intervention strategy generation module 4 and feedback optimization module 5. The system not only can monitor the health status of the patient in real time, but also can generate personalized intervention scheme according to individual characteristics, and continuously improve the accuracy of prediction and intervention through feedback optimization mechanism. This multi-module collaborative mechanism provides an intelligent solution for modern medicine, significantly improving the efficiency and effect of disease prediction and intervention. In order to better enable relevant personnel in this technical field to fully understand and implement the present application, the specific implementation principles of the present application are supplemented in the following by combining a specific application scenario.

[0045] In practical application, assume that patient A is a 45-year-old male with a history of hypertension and irregular living and working habits. Through the disease prediction and intervention system of the present application, the specific steps are as follows:

[0046] First, the health data acquisition module 1 starts running. The wearable device 6 (such as a smart bracelet) is built-in with multiple sensors to monitor the patient's A's heart rate, blood pressure, and blood oxygen saturation and other physiological indicators in real time. These sensors transmit the collected data to the microcontroller through the hardware circuit, and the microcontroller processes the data preliminarily and sends it to the mobile terminal 7 (such as a smart phone) through Bluetooth or Wi-Fi. At the same time, the application program on the mobile terminal 7 records the patient's A's daily behavior data through the user interaction interface, such as dietary intake, exercise duration and sleep quality. These multi-source data are then integrated and uploaded to the cloud server 8 through the Internet. The cloud server 8 uses Hadoop distributed file system to store these heterogeneous data, and uses Spark framework for efficient processing, and finally forms a complete health record for subsequent modules. This process ensures the comprehensiveness and continuity of data acquisition, thereby laying a foundation for subsequent analysis.

[0047] Second, the feature extraction module 2 processes the standardized health data in the cloud server 8. Module 2 first calculates the mean and standard deviation of each column of data, and converts the original data into a standard normal distribution form with mean 0 and standard deviation 1 to eliminate dimensional differences. Then, module 2 calculates the covariance matrix and its eigenvalues and eigenvectors through principal component analysis algorithm, selects the eigenvectors corresponding to the first k largest eigenvalues as principal components, and completes the dimensionality reduction. On this basis, the random forest algorithm sorts the importance of the dimensionality-reduced feature variables, and selects the feature variables highly related to disease risk, such as the patient's systolic blood pressure fluctuation range and nighttime sleep duration. These key feature variables are then passed to the risk assessment module 3.

[0048] Subsequently, the risk assessment module 3 performs quantitative analysis on the selected key feature variables based on the deep neural network model 9. The input layer of the deep neural network model 9 receives the feature variables output by the feature extraction module 2, and after multiple layers of nonlinear transformation, the output layer generates a risk coefficient between 0 and 1. This coefficient represents the probability of patient A developing cardiovascular disease in a specific future time period. If the prediction performance of the model does not meet expectations, module 3 will adjust hyperparameters (such as learning rate or number of hidden layers) until the accuracy requirement is met. In this scenario, assume the risk coefficient output by the model is 0.75, indicating that patient A belongs to a high-risk group.

[0049] Next, the intervention strategy generation module 4 develops a personalized intervention plan based on the risk assessment results. Module 4 first classifies patient A as a high-risk group, then calls the pre-set intervention rule library to generate a preliminary intervention plan. For example, the rule library may recommend that patient A increase the frequency of aerobic exercise to 5 times a week, reduce high-salt diet intake, and take antihypertensive drugs regularly. Subsequently, module 4 combines patient A's individual characteristics (such as age, gender, and medical history) to optimize the preliminary intervention plan through genetic algorithms. Genetic algorithms first binary code the preliminary plan to form an initial population; then evaluate the merits of each individual through the fitness function; then generate a new generation of population through selection, crossover, and mutation operations; finally, after several generations of evolution, the optimal solution is selected as the final intervention strategy. In this scenario, the intervention plan optimized by the genetic algorithm suggests that patient A perform 30 minutes of brisk walking every day and reduce high-salt food intake at night.

[0050] After patient A executes the intervention plan, the feedback optimization module 5 begins to collect relevant feedback data. Module 5 defines the state space (including the patient's health status and intervention execution), action space (possible intervention measure adjustment options), and reward function (designed according to the degree of health improvement and compliance) through reinforcement learning algorithms. Update the policy function through the Q-learning algorithm to gradually approach the optimal strategy. For example, if patient A's blood pressure stabilizes and nighttime sleep quality improves significantly after executing the intervention plan, the system will automatically increase the recommended frequency of such intervention measures. At the same time, the feedback optimization module 5 dynamically adjusts the parameters of the deep neural network model 9 of the risk assessment module 3 and the content of the intervention rule library of the intervention strategy generation module 4, thereby further improving the accuracy of prediction and intervention.

[0051] The above steps are cyclically operated in a closed loop system. Taking patient A as an example, when he continuously uses the system, the health data acquisition module 1 continuously acquires the latest physiological and behavioral data, the feature extraction module 2 reselects the key feature variables, the risk assessment module 3 updates the disease risk coefficient, the intervention strategy generation module 4 adjusts the personalized scheme, and the feedback optimization module 5 optimizes the overall performance of the system according to the latest feedback data. This closed loop mechanism ensures that the system can adapt to the actual needs of the patient and continuously improve the prediction and intervention effect.

[0052] In addition, the multi-module collaborative mechanism of the present application exhibits significant technical advantages in practical application. For example, the wearable device 6 of the health data acquisition module 1 and the mobile terminal 7 work together to realize real-time acquisition and integration of health data; the feature extraction module 2 combines principal component analysis algorithm and random forest algorithm to significantly improve the efficiency and accuracy of feature selection; the deep neural network model 9 of the risk assessment module 3 can more accurately quantify the disease risk through back propagation algorithm and hyperparameter adjustment; the intervention strategy generation module 4 combines intervention rule library and genetic algorithm to ensure the scientificity and individualization of the intervention scheme; the feedback optimization module 5 realizes the self-adaptive ability of the system through reinforcement learning algorithm, which significantly improves the overall effect.

[0053] In summary, the present application constructs an intelligent disease prediction and intervention system through the collaborative work of the health data acquisition module 1, the feature extraction module 2, the risk assessment module 3, the intervention strategy generation module 4 and the feedback optimization module 5. The system not only can monitor the health status of the patient in real time, but also can generate individualized intervention scheme according to individual characteristics, and continuously improve the accuracy of prediction and intervention through feedback optimization mechanism, which provides an intelligent solution for modern medicine.

Claims

1. A disease prediction and intervention method combining artificial intelligence, characterized in that, Includes the following steps: Step S1. Data Acquisition: Obtain multi-dimensional health data of patients through the health data acquisition module; Step S2. Feature Extraction: Process the acquired data using the feature extraction module to extract key features; Step S3. Risk Assessment: Based on the extracted features, the risk coefficient for disease occurrence is calculated through the risk assessment module; Step S4. Intervention Generation: Based on the risk assessment results, the intervention strategy generation module is invoked to generate a personalized intervention plan; Step S5. Execution Feedback: After the patient executes the intervention plan, feedback data is collected through the feedback optimization module; Step S6. System Optimization: Based on the feedback data, the risk assessment model and intervention rule base are iteratively optimized.

2. The disease prediction and intervention method combining artificial intelligence according to claim 1, characterized in that, The health data acquisition module consists of a wearable device, a mobile terminal, and a cloud server. The wearable device monitors the patient's physiological indicators in real time and transmits the data to the mobile terminal. The mobile terminal records the patient's daily behavior data and uploads the integrated health data to the cloud server. The cloud server stores and integrates all the data to form a complete health record.

3. The disease prediction and intervention method combining artificial intelligence according to claim 1, characterized in that, The feature extraction module uses principal component analysis to reduce the dimensionality of high-dimensional data and uses random forest algorithm to sort the importance of feature variables in order to screen out feature variables that are highly correlated with disease risk.

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