Abnormal health event prediction system based on AI

Abstract

The invention relates to the technical field of health event prediction, in particular to an AI-based abnormal health event prediction system, which comprises a data acquisition module, a data preprocessing module, a causal analysis and health trend deduction module, a dynamic risk assessment and hierarchical management module, a feedback and optimization module and a user interaction and suggestion module, the data acquisition module collects health data of a user in real time; the data preprocessing module is used for preprocessing the collected health data; the causal analysis and health trend deduction module deduces the health trend of the user; the dynamic risk assessment and level-to-level management module dynamically assesses the health risk of the user; and the feedback and optimization module optimizes the health trend prediction model. According to the method and the system, the user is helped to take proper actions to prevent or deal with potential health problems, refined and personalized health management is realized, and the overall health level of the user is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of health event prediction, and in particular to an abnormal health event prediction system based on AI. Background Art

[0002] With the development of social economy and changes in lifestyle, people's demand for health management is increasing. In particular, on a global scale, chronic diseases, mental health problems and sudden health events have gradually become the main factors threatening public health. Health management systems based on artificial intelligence (AI) have gradually become an important tool for personalized health management because they can monitor, analyze and predict users' health status in real time. By collecting and analyzing large amounts of health data, these systems can identify health trends, assess potential risks, and provide users with scientific health management recommendations.

[0003] However, the existing health management systems still have some shortcomings in data collection, risk assessment and model optimization. First, the data collection channels of the existing systems are limited, and it is difficult to fully cover the user's physiological, environmental, living habits and psychological state data, which affects the accuracy of health assessment. Secondly, most systems often rely only on simple statistical models or rules when conducting health risk assessments, and fail to fully consider the complexity and dynamic changes of health data, making it difficult to provide accurate risk assessments and personalized management recommendations. In addition, the existing systems usually lack an effective model optimization mechanism, making it difficult to self-adjust based on real-time feedback information from users, resulting in insufficient adaptability and accuracy of health prediction models in long-term use.

[0004] In view of the above-mentioned shortcomings, the present invention aims to provide an AI-based abnormal health event prediction system to provide users with personalized and dynamically adjusted health management suggestions, thereby achieving early warning and effective intervention of potential health risks and improving the overall health management effect of users. Summary of the invention

[0005] Based on the above objectives, the present invention provides an abnormal health event prediction system based on AI.

[0006] The AI-based abnormal health event prediction system includes data collection module, data preprocessing module, causal analysis and health trend deduction module, dynamic risk assessment and hierarchical management module, feedback and optimization module, and user interaction and suggestion module, among which;

[0007] The data collection module collects the user's health data in real time, including physiological indicators, environmental data, living habits data and psychological state data;

[0008] The data preprocessing module preprocesses the collected health data, including cleaning, denoising, and standardization;

[0009] The causal analysis and health trend deduction module identifies the potential causal relationship of health events based on the pre-processed health data, and deduces the user's health trend through the health trend prediction model;

[0010] The dynamic risk assessment and hierarchical management module dynamically assesses the user's health risk based on the deduced user health trend and the real-time changes in current health data, and divides the user's health risk into multiple levels, and provides hierarchical health management suggestions and intervention measures according to different risk levels, including:

[0011] Real-time data monitoring: continuously monitor the user's health data changes and obtain the latest health data in real time;

[0012] Risk assessment calculation: Based on the inferred user health trends and current health data, the user's current health risk level is calculated through a real-time risk assessment algorithm;

[0013] Risk level classification: Based on the calculated current health risk level, the user's health risk is divided into multiple levels, including low risk, medium risk and high risk;

[0014] Health management suggestions and intervention measures: Generate health management suggestions and intervention measures based on the user's health risk level;

[0015] The feedback and optimization module receives user feedback information and optimizes the health trend prediction model;

[0016] The user interaction and suggestion module feeds back the prediction results of health trends, health risk assessment, and health management suggestions and intervention measures to the user.

[0017] Optionally, the data acquisition module includes:

[0018] Multi-sensor interface: By connecting with a variety of health monitoring devices (wearable devices, biosensors, smartphones), real-time collection of users' physiological indicators data, including heart rate, blood pressure, body temperature and respiratory rate;

[0019] Environmental data collection: By integrating environmental sensors (temperature sensor, humidity sensor, air quality sensor, noise sensor), the temperature, humidity, air quality and noise level of the user's environment can be monitored in real time;

[0020] Lifestyle data collection: Automatically record the user's lifestyle data, including diet, exercise, sleep and daily activity patterns, by interacting with the user's smart devices (smart bracelets, mobile phone applications);

[0021] Psychological state data collection: Collect users' emotional and psychological state data through voice analysis, facial expression recognition and user self-assessment questionnaires.

[0022] Optionally, the data preprocessing module includes:

[0023] Data cleaning: Identify and handle missing values ​​and outliers using interpolation methods;

[0024] Data denoising: Use wavelet transform to denoise health data, decompose the data into multi-scale components, and remove high-frequency noise;

[0025] Data standardization: The denoised health data were standardized using the Z-score standardization method.

[0026] Optionally, the causal analysis and health trend deduction module includes:

[0027] Causal relationship analysis: Based on pre-processed health data, causal inference algorithms are used to identify potential causal relationships between health events;

[0028] Health trend prediction: Based on the potential causal relationship between identified health events, a health trend prediction model is constructed to deduce the user's health trend.

[0029] Optionally, the causal relationship analysis includes:

[0030] Data input: receiving pre-processed health data as input data for the causal inference algorithm;

[0031] Causal relationship identification: Based on the input health data, the Granger causal analysis algorithm is used to identify the potential causal relationship between health events, which is expressed as:

[0032]

[0033] Among them, y t and x t are two time series, p and q are the lag orders, α i and β j is the regression coefficient, ∈ t is the error term;

[0034] Causal network construction: Based on the results of causal relationship identification, a causal network between health events is constructed using a Bayesian network, which is expressed as:

[0035]

[0036] Among them, X1, X2, …, X n represents n variables in health data (such as physiological indicators, environmental data, etc.), P(Xi |Pa(X i )) indicates that given X i The parent node set Pa(X i ), the parent node refers to the conditional probability distribution of X i Variables with direct causal influence, Pa(X i ) is node X i The parent node collection of .

[0037] Optionally, the health trend prediction model adopts a Prophet model, and the Prophet model includes:

[0038] Trend model optimization: A combination of piecewise linear trend and multi-level trend is introduced to capture the complex changes in health data, expressed as:

[0039]

[0040] Among them, g(t) is the trend function, k i and a i are the slope and intercept of the i-th segment, t i It's time for change. is the indicator function, c j is the polynomial coefficient of the jth segment, p j is the polynomial order, is the corresponding change point, m is the number of segments for piecewise linear trend, and n is the number of segments for polynomial trend;

[0041] Enhanced periodic and seasonal components: Multi-period combination and modulation of environmental factors are introduced to enhance the flexibility of seasonal components and capture the periodic fluctuations in health data caused by environmental changes, expressed as:

[0042]

[0043] Where s(t) is the seasonal function, K is the number of cycles, and P k is the length of the kth period, α k and β k is the amplitude coefficient of the kth period, γ k is the modulation factor, E(t) represents the environmental factors related to health (such as temperature, air quality, etc.);

[0044] Holiday effect and personalized event processing: Holiday effect and personalized event processing are introduced to identify and quantify the impact of holidays or personalized events on health data, expressed as:

[0045]

[0046] Among them, h(t) is the holiday and event effect function, L is the number of holidays or festivals, δ l is the impact coefficient of the lth holiday or festival, D l is the set of holidays or festivals, Q is the number of personalized events, λ q is the influence coefficient of the qth personalized event, e q is the time of occurrence of the corresponding health event;

[0047] Uncertainty processing of health data: A time-dependent noise model is introduced to model the volatility and uncertainty of health data, and a confidence interval is provided for each predicted value, expressed as:

[0048] y(t)=g(t)+s(t)+h(t)+∈(t);

[0049]

[0050] Where y(t) is the predicted value of the health data, ∈(t) is Gaussian white noise, σ 2 (t) represents the time dependence of the noise;

[0051] Dynamic adjustment mechanism for health status: A dynamic adjustment mechanism is introduced to continuously update the parameters of the Prophet model to adapt to changes in health data, expressed as:

[0052] θ t+1 =θ t +η·Δθ(t);

[0053] Among them, θ t represents the current parameters of the model, η is the learning rate, and Δθ(t) is the parameter adjustment calculated based on the current health data changes.

[0054] Optionally, the risk assessment calculation includes:

[0055] Health trend input: Receive the predicted value y(t) of the user's health data and the current health data X(t) as the basic input for risk assessment calculation;

[0056] Risk assessment: Use the Logistic regression model to calculate the user's current health risk level R t , expressed as:

[0057]

[0058] Among them, P(R t |y(t),X(t)) is the user’s current health risk R given the health trend prediction result y(t) and the current health data X(t) tis the probability of high risk, β0 is the intercept term, β1 is the regression coefficient of the health trend prediction result y(t), β2 is the regression coefficient of the current health data X(t), and exp(·) represents the exponential function.

[0059] Optionally, the risk level classification includes:

[0060] Risk threshold setting: pre-define multiple risk level threshold ranges, including low risk, medium risk and high risk threshold intervals, expressed as:

[0061] [R low ,R medium ,R high ];

[0062] Risk level determination: Based on the calculated current health risk level P(R t |y(t),X(t)), the user's health risk is divided into corresponding levels, expressed as:

[0063] Low risk level: P(R t |y(t),X(t))≤R low ;

[0064] Medium risk level: R low <P(Rt|y(t),X(t))≤R medium ;

[0065] High risk level: P(R t |y(t),X(t))>R medium .

[0066] Optionally, the health management suggestions and intervention measures include:

[0067] Recommended rule setting: According to different health risk levels (low risk, medium risk, high risk), a rule base of corresponding health management suggestions and intervention measures is pre-set, including lifestyle adjustments, preventive measures and medical advice;

[0068] Recommendation generation: Based on the user's current health risk level, the corresponding rules in the recommendation rule library are called to generate personalized health management recommendations and intervention measures, including:

[0069] Low-risk level recommendations: Generate lifestyle adjustment recommendations focused on maintaining health, including a balanced diet, moderate exercise, and good sleep;

[0070] Medium-risk level recommendations: Generate recommendations for preventive measures and enhanced health management, including regular physical examinations, increased exercise, weight control, and monitoring of blood pressure or blood sugar;

[0071] High-risk level recommendations: Generate urgent medical recommendations and key health monitoring measures, including immediate medical attention, adjustment of medication regimens, medical examinations and follow-up.

[0072] Optionally, the feedback and optimization module includes:

[0073] Feedback information reception: receiving user feedback information, including changes in the user's health status, the effects of adopting health management suggestions, the user's self-perceived health improvements, and health data provided by the user;

[0074] Model optimization: Based on the received user feedback information, the health trend prediction model is optimized using an incremental learning algorithm, including:

[0075] Weight adjustment:

[0076] Among them, w old and w new Respectively represent the model weights before and after the update, η is the learning rate, is the gradient of the loss function L with respect to the weight;

[0077] Parameter update:

[0078] Among them, θ old and θ new Respectively represent the model parameters before and after the update, γ is the parameter learning rate, is the partial derivative of the loss function L with respect to the model parameters;

[0079] Algorithm modification: y new (t) = f(X(t); θ new )+Δy;

[0080] Among them, f(X(t);θ new ) is the optimized parameter θ new The recalculated health trend prediction result, Δy, is the adjustment amount based on user feedback information.

[0081] Beneficial effects of the present invention:

[0082] The present invention provides high-quality basic data for subsequent health event prediction and risk assessment through cleaning, denoising and standardization processing of the data preprocessing module, thereby significantly improving the accuracy and reliability of the model.

[0083] The present invention, through the causal analysis and health trend deduction module, combined with the Prophet model, can accurately capture the potential causal relationship between health events, and deduce the user's health trend through the optimized health trend prediction model. The dynamic risk assessment and graded management module calculates the health risk level through the Logistic regression model, and grades the user's health risk level according to the threshold set by historical data, providing accurate risk assessment and personalized health management suggestions.

[0084] The present invention receives real-time feedback information from users and dynamically adjusts the health trend prediction model to ensure the long-term adaptability and reliability of the model. The health management advice and intervention measures module generates personalized health management advice based on the user's risk level to help the user take appropriate actions to prevent or respond to potential health problems, thereby achieving refined and personalized health management and improving the user's overall health level. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0086] Figure 1 A schematic diagram of system function modules according to an embodiment of the present invention;

[0087] Figure 2 Schematic diagram of a dynamic risk assessment and hierarchical management module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0088] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0089] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0090] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0091] like Figure 1-Figure 2 As shown, the AI-based abnormal health event prediction system includes a data acquisition module, a data preprocessing module, a causal analysis and health trend deduction module, a dynamic risk assessment and hierarchical management module, a feedback and optimization module, and a user interaction and suggestion module, among which;

[0092] The data collection module collects the user's health data in real time, including physiological indicators, environmental data, living habits data and psychological state data;

[0093] The data preprocessing module preprocesses the collected health data, including cleaning, denoising, and standardization to ensure the accuracy and consistency of the input data;

[0094] The causal analysis and health trend deduction module identifies the potential causal relationship of health events based on pre-processed health data, and deduces the user's health trend through the health trend prediction model;

[0095] The dynamic risk assessment and graded management module dynamically assesses the user's health risk based on the deduced user health trend and the real-time changes in current health data, and divides the user's health risk into multiple levels. It provides graded health management suggestions and intervention measures based on different risk levels, including:

[0096] Real-time data monitoring: continuously monitor the user's health data changes and obtain the latest health data in real time;

[0097] Risk assessment calculation: Based on the inferred user health trends and current health data, the user's current health risk level is calculated through a real-time risk assessment algorithm;

[0098] Risk level classification: Based on the calculated current health risk level, the user's health risk is divided into multiple levels, including low risk, medium risk and high risk;

[0099] Health management suggestions and intervention measures: Generate health management suggestions and intervention measures based on the user's health risk level;

[0100] The feedback and optimization module receives user feedback information, optimizes the health trend prediction model, and improves the accuracy and adaptability of the model;

[0101] The user interaction and suggestion module provides users with feedback on health trend prediction results, health risk assessment, health management suggestions and intervention measures to help users better understand and manage their own health;

[0102] Through the above content, not only can potential health risks be identified and personalized management suggestions be provided, but real-time feedback and optimization functions are also provided to ensure the continued accuracy and adaptability of the prediction model, thereby providing users with more targeted and effective health management services.

[0103] The data acquisition module includes:

[0104] Multi-sensor interface: By connecting with a variety of health monitoring devices (wearable devices, biosensors, smartphones), real-time collection of users' physiological indicators data, including heart rate, blood pressure, body temperature and respiratory rate;

[0105] Environmental data collection: By integrating environmental sensors (temperature sensor, humidity sensor, air quality sensor, noise sensor), the temperature, humidity, air quality and noise level of the user's environment can be monitored in real time;

[0106] Lifestyle data collection: Automatically record the user's lifestyle data, including diet, exercise, sleep and daily activity patterns, by interacting with the user's smart devices (smart bracelets, mobile phone applications);

[0107] Psychological state data collection: Collect user's emotional and psychological state data through voice analysis, facial expression recognition and user self-assessment questionnaire;

[0108] Through the above content, the richness and accuracy of the data are ensured, which in turn provides strong support for the system's health event prediction and personalized health management, can more accurately reflect the user's overall health status, and help to detect potential health risks early.

[0109] The data preprocessing module includes:

[0110] Data cleaning: Use interpolation to identify and handle missing values ​​and outliers, expressed as:

[0111]

[0112] Among them, x i For missing data, x i-1 and x i+1 are adjacent valid data points;

[0113] Data denoising: Use wavelet transform to denoise health data, decompose the data into multi-scale components, and remove high-frequency noise, expressed as:

[0114]

[0115] in, is the denoised health data, N is the number of layers or levels of wavelet decomposition, k is the index of wavelet coefficients and basis functions, ranging from 0 to N-1, is the wavelet coefficient after denoising, ψ k (t) is the wavelet basis function, is the denoised data;

[0116] Data standardization: The denoised health data were standardized by the Z-score standardization method to eliminate the impact of different data scales on subsequent analysis, expressed as:

[0117]

[0118] Among them, x is the original data, μ is the mean of the data, σ is the standard deviation of the data, and z is the standardized data;

[0119] The above content provides reliable basic data for subsequent health event prediction and risk assessment, significantly improving the accuracy and reliability of the model.

[0120] The causal analysis and health trend deduction modules include:

[0121] Causal relationship analysis: Based on pre-processed health data, causal inference algorithms are used to identify potential causal relationships between health events, determine the impact of health factors, and provide causal clues for health trend prediction;

[0122] Health trend prediction: Based on the potential causal relationship between identified health events, a health trend prediction model is constructed to deduce the user's health trend;

[0123] Through the above content, we ensure that the prediction model is not only based on the correlation in the data, but also accurately reflects the actual impact path of health factors, making the prediction results more interpretable and accurate, helping users better understand the changes in their health status and their causes, thereby supporting more accurate health management and early intervention measures.

[0124] Causal analysis includes:

[0125] Data input: receiving pre-processed health data as input data for the causal inference algorithm;

[0126] Causal relationship identification: Based on the input health data, the Granger causal analysis algorithm is used to identify the potential causal relationship between health events, which is expressed as:

[0127]

[0128] Among them, y t and x t are two time series, p and q are the lag orders, α i and β j is the regression coefficient, ∈ t is the error term, if the sequence x t Significantly improved t prediction, then we assume that x t Yes t Granger causality;

[0129] Causal network construction: Based on the results of causal relationship identification, a causal network between health events is constructed using a Bayesian network, which is expressed as:

[0130]

[0131] Among them, X1, X2, …, X n represents n variables in health data (such as physiological indicators, environmental data, etc.), P(X i |Pa(X i )) indicates that given X i The parent node set Pa(X i ), the parent node refers to the conditional probability distribution of X i Variables with direct causal influence, Pa(X i ) is node X i The parent node set of i All nodes of

[0132] Through the above content, not only the reliability of the model is improved, but also in-depth causal analysis support is provided for users' health management, helping to identify key health factors and then take more targeted prevention and intervention measures.

[0133] The health trend prediction model adopts the Prophet model, which includes:

[0134] Trend model optimization: A combination of piecewise linear trend and multi-level trend is introduced to capture the complex changes in health data and improve the fitting effect of the model, which is expressed as:

[0135]

[0136] Among them, g(t) is the trend function, k i and a i are the slope and intercept of the i-th segment, t i It's time for change. is the indicator function, when t≥t i 1 when the value is 0, otherwise cj is the polynomial coefficient of the jth segment, p j is the polynomial order, is the corresponding change point, m is the number of segments for piecewise linear trend, and n is the number of segments for polynomial trend;

[0137] Enhanced periodic and seasonal components: Multi-period combination and modulation of environmental factors are introduced to enhance the flexibility of seasonal components and capture the periodic fluctuations in health data caused by environmental changes, expressed as:

[0138]

[0139] Where s(t) is the seasonal function, K is the number of cycles, and P k is the length of the kth period, α k and β k is the amplitude coefficient of the kth period, γ k is the modulation factor, E(t) represents the environmental factors related to health (such as temperature, air quality, etc.);

[0140] Holiday effect and personalized event processing: Holiday effect and personalized event processing are introduced to identify and quantify the impact of holidays or personalized events on health data, thereby improving the accuracy of prediction, which is expressed as:

[0141]

[0142] Among them, h(t) is the holiday and event effect function, L is the number of holidays or festivals, δ l is the impact coefficient of the lth holiday or festival, D l is the set of holidays or festivals, Q is the number of personalized events, λ q is the influence coefficient of the qth personalized event, e q is the time of occurrence of the corresponding health event;

[0143] Uncertainty processing of health data: A time-dependent noise model is introduced to model the volatility and uncertainty of health data, and a confidence interval is provided for each predicted value, thereby improving the reliability of the prediction, which is expressed as:

[0144] y(t)=g(t)+s(t)+h(t)+∈(t);

[0145]

[0146] Where y(t) is the predicted value of the health data, ∈(t) is Gaussian white noise, σ 2 (t) represents the time dependence of the noise;

[0147] Dynamic adjustment mechanism for health status: A dynamic adjustment mechanism is introduced to continuously update the parameters of the Prophet model to adapt to changes in health data, thereby maintaining the accuracy and timeliness of the prediction, expressed as:

[0148] θ t+1 =θ t +η·Δθ(t);

[0149] Among them, θ t represents the current parameters of the model, η is the learning rate, and Δθ(t) is the parameter adjustment calculated based on the current health data changes;

[0150] Through the above content, it can better adapt to the complexity and variability of health data. These improvements make the model more accurate and reliable in health trend prediction, providing more targeted prediction support for health management.

[0151] Risk assessment calculations include:

[0152] Health trend input: Receive the predicted value y(t) of the user's health data and the current health data X(t) as the basic input for risk assessment calculation;

[0153] Risk assessment: Use the Logistic regression model to calculate the user's current health risk level R t , expressed as:

[0154]

[0155] Among them, P(R t |y(t),X(t)) is the user’s current health risk R given the health trend prediction result y(t) and the current health data X(t) t is the probability of high risk, β0 is the intercept term, β1 is the regression coefficient of the health trend prediction result y(t), β2 is the regression coefficient of the current health data X(t), and exp(·) represents the exponential function;

[0156] The above content can more accurately reflect the user's overall health risk, help with early warning and personalized health management, and provide users with more reliable health risk assessment.

[0157] Risk level classification includes:

[0158] Risk threshold setting: pre-define multiple risk level threshold ranges, including low risk, medium risk and high risk threshold intervals, expressed as:

[0159] [R low ,R medium ,R high ];

[0160] Risk level determination: Based on the calculated current health risk level P(R t |y(t),X(t)), the user's health risk is divided into corresponding levels, expressed as:

[0161] Low risk level: P(R t |y(t),X(t))≤R low ;

[0162] Medium risk level: R low <P(R t |y(t),X(t))≤R medium ;

[0163] High risk level: P(R t |y(t),X(t))>R medium ;

[0164] Through the above content, the user's health risks are ensured to be accurately identified and can provide clear guidance for subsequent health management measures.

[0165] Risk thresholds are set based on statistical analysis of historical health data, including:

[0166] Data collection: Collect historical health data of a large number of users, including long-term health trends, current health data and corresponding health events (such as disease diagnosis, hospitalization records, etc.);

[0167] Risk assessment model training: Use the collected historical data to train and validate the Logistic regression model to calculate the health risk level of each user at various time points;

[0168] Event association analysis: associate historical health events (such as disease onset, health crisis) with the health risk level at the corresponding time point to find out the frequency and severity of health events at different health risk levels;

[0169] Threshold setting: The low risk threshold is set as the risk level with low incidence and severity of health events, that is, the health status corresponding to the risk prediction below this level is relatively stable; the medium risk threshold is set as the risk level where the incidence and severity of health events begin to increase significantly, that is, the health status corresponding to this level has certain risks and preventive measures need to be taken; the high risk threshold is set as the risk level with high incidence and severity of health events, that is, the health status corresponding to this level has great risks and may need immediate intervention;

[0170] Threshold verification and adjustment: Use new data sets for verification to check the effectiveness of these thresholds. If it is found in actual applications that the distribution of health events does not match expectations, further adjust the thresholds to better meet the user's health management needs.

[0171] Health management recommendations and interventions include:

[0172] Recommended rule setting: According to different health risk levels (low risk, medium risk, high risk), a rule base of corresponding health management suggestions and intervention measures is pre-set, including lifestyle adjustments, preventive measures and medical advice;

[0173] Recommendation generation: Based on the user's current health risk level, the corresponding rules in the recommendation rule library are called to generate personalized health management recommendations and intervention measures, including:

[0174] Low-risk level recommendations: Generate lifestyle adjustment recommendations focused on maintaining health, including a balanced diet, moderate exercise, and good sleep;

[0175] Medium-risk level recommendations: Generate recommendations for preventive measures and enhanced health management, including regular physical examinations, increased exercise, weight control, and monitoring of blood pressure or blood sugar;

[0176] High-risk level recommendations: Generate urgent medical recommendations and key health monitoring measures, including immediate medical attention, adjustment of medication regimens, medical examinations and follow-up;

[0177] Through the above content, it is ensured that each user can receive advice that matches his or her current health status, helping users take appropriate actions to prevent or respond to potential health problems, thereby achieving personalized health management and risk intervention.

[0178] The feedback and optimization module includes:

[0179] Feedback information reception: receiving user feedback information, including changes in the user's health status, the effects of adopting health management suggestions, the user's self-perceived health improvements, and health data provided by the user;

[0180] Model optimization: Based on the received user feedback information, the health trend prediction model is optimized using an incremental learning algorithm, including:

[0181] Weight adjustment:

[0182] Among them, w old and w new Respectively represent the model weights before and after the update, η is the learning rate, is the gradient of the loss function L with respect to the weight, which is calculated based on user feedback information and represents the error between the model prediction result y(t) and the real health data;

[0183] Parameter update:

[0184] Among them, θ old and θ new Respectively represent the model parameters before and after the update, γ is the parameter learning rate, is the partial derivative of the loss function L with respect to the model parameters;

[0185] Algorithm modification: y new (t) = f(X(t); θ new )+Δy;

[0186] Among them, f(X(t);θ new ) is the optimized parameter θ new The recalculated health trend prediction result, Δy, is the adjustment amount based on user feedback information;

[0187] Through the above content, it is possible to more accurately capture the user's health trends and risks, provide more targeted health management recommendations and intervention measures, ensure the long-term adaptability and reliability of the model, and provide continuous support for the user's health management.

[0188] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0189] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. AI-based abnormal health event prediction system, characterized by: It includes data collection module, data preprocessing module, causal analysis and health trend deduction module, dynamic risk assessment and classification management module, feedback and optimization module and user interaction and suggestion module, among which; The data collection module collects the user's health data in real time, including physiological indicators, environmental data, living habits data and psychological state data; The data preprocessing module preprocesses the collected health data, including cleaning, denoising, and standardization; The causal analysis and health trend deduction module identifies the potential causal relationship of health events based on the pre-processed health data, and deduces the user's health trend through the health trend prediction model; The dynamic risk assessment and hierarchical management module dynamically assesses the user's health risk based on the deduced user health trend and the real-time changes in current health data, and divides the user's health risk into multiple levels, and provides hierarchical health management suggestions and intervention measures according to different risk levels, including: Real-time data monitoring: continuously monitor the user's health data changes and obtain the latest health data in real time; Risk assessment calculation: Based on the inferred user health trends and current health data, the user's current health risk level is calculated through a real-time risk assessment algorithm; Risk level classification: Based on the calculated current health risk level, the user's health risk is divided into multiple levels, including low risk, medium risk and high risk; Health management suggestions and intervention measures: Generate health management suggestions and intervention measures based on the user's health risk level; The feedback and optimization module receives user feedback information and optimizes the health trend prediction model; The user interaction and suggestion module feeds back the prediction results of health trends, health risk assessment, and health management suggestions and intervention measures to the user.

2. The abnormal health event prediction system based on AI according to claim 1 is characterized in that: The data acquisition module comprises: Multi-sensor interface: By connecting with a variety of health monitoring devices, it can collect the user's physiological indicators in real time, including heart rate, blood pressure, body temperature and respiratory rate; Environmental data collection: By integrating environmental sensors, the temperature, humidity, air quality and noise level of the user's environment can be monitored in real time; Lifestyle data collection: Automatically record the user's lifestyle data, including diet, exercise, sleep and daily activity patterns, by interacting with the user's smart device; Psychological state data collection: Collect users' emotional and psychological state data through voice analysis, facial expression recognition and user self-assessment questionnaires.

3. The abnormal health event prediction system based on AI according to claim 1 is characterized in that: The data preprocessing module comprises: Data cleaning: Identify and handle missing values ​​and outliers using interpolation methods; Data denoising: Use wavelet transform to denoise health data, decompose the data into multi-scale components, and remove high-frequency noise; Data standardization: The denoised health data were standardized using the Z-score standardization method.

4. The AI-based abnormal health event prediction system according to claim 1, characterized in that: The causal analysis and health trend deduction module includes: Causal relationship analysis: Based on pre-processed health data, causal inference algorithms are used to identify potential causal relationships between health events; Health trend prediction: Based on the potential causal relationship between identified health events, a health trend prediction model is constructed to deduce the user's health trend.

5. The AI-based abnormal health event prediction system according to claim 4, characterized in that: The causal relationship analysis includes: Data input: receiving pre-processed health data as input data for the causal inference algorithm; Causal relationship identification: Based on the input health data, the Granger causal analysis algorithm is used to identify the potential causal relationship between health events, which is expressed as: Among them, y t and x t are two time series, p and q are the lag orders, α i and β j is the regression coefficient, ∈ t is the error term; Causal network construction: Based on the results of causal relationship identification, a causal network between health events is constructed using a Bayesian network, which is expressed as: Among them, X1, X2, …, X n represents n variables in health data, P(X i |Pa(X i )) indicates that given X i The parent node set Pa(X i ), the parent node refers to the conditional probability distribution of X i Variables with direct causal influence, Pa(X i ) is node X i The parent node collection of .

6. The AI-based abnormal health event prediction system according to claim 5, characterized in that: The health trend prediction model adopts the Prophet model, and the Prophet model includes: Trend model optimization: A combination of piecewise linear trend and multi-level trend is introduced to capture the complex changes in health data, expressed as: Among them, g(t) is the trend function, k i and a i are the slope and intercept of the i-th segment, t i It's time for change. is the indicator function, c j is the polynomial coefficient of the jth segment, p j is the polynomial order, is the corresponding change point, m is the number of segments for piecewise linear trend, and n is the number of segments for polynomial trend; Enhanced periodic and seasonal components: Multi-period combination and modulation of environmental factors are introduced to enhance the flexibility of seasonal components and capture the periodic fluctuations in health data caused by environmental changes, expressed as: Where s(t) is the seasonal function, K is the number of cycles, and P k is the length of the kth period, α k and β k is the amplitude coefficient of the kth period, γ k is the modulation factor, E(t) represents the environmental factor related to health; Holiday effect and personalized event processing: Holiday effect and personalized event processing are introduced to identify and quantify the impact of holidays or personalized events on health data, expressed as: Among them, h(t) is the holiday and event effect function, L is the number of holidays or festivals, δ l is the impact coefficient of the lth holiday or festival, D l is the set of holidays or festivals, Q is the number of personalized events, λ q is the influence coefficient of the qth personalized event, e q is the time of occurrence of the corresponding health event; Uncertainty processing of health data: A time-dependent noise model is introduced to model the volatility and uncertainty of health data, and a confidence interval is provided for each predicted value, expressed as: y(t)=g(t)+s(t)+h(t)+∈(t); Where y(t) is the predicted value of the health data, ∈(t) is Gaussian white noise, σ 2 (t) represents the time dependence of the noise; Dynamic adjustment mechanism for health status: A dynamic adjustment mechanism is introduced to continuously update the parameters of the Prophet model to adapt to changes in health data, expressed as: i t+1 =θ t +η·Δθ(t); Among them, θ t represents the current parameters of the model, η is the learning rate, and Δθ(t) is the parameter adjustment calculated based on the current health data changes.

7. The AI-based abnormal health event prediction system according to claim 6, characterized in that: The risk assessment calculations include: Health trend input: Receive the predicted value y(t) of the user's health data and the current health data X(t) as the basic input for risk assessment calculation; Risk assessment: Use the Logistic regression model to calculate the user's current health risk level R t , expressed as: Among them, P(R t |y(t),X(t)) is the user’s current health risk R given the health trend prediction result y(t) and the current health data X(t) t is the probability of high risk, β0 is the intercept term, β1 is the regression coefficient of the health trend prediction result y(t), β2 is the regression coefficient of the current health data X(t), and exp(·) represents the exponential function.

8. The AI-based abnormal health event prediction system according to claim 7, characterized in that: The risk level classification includes: Risk threshold setting: pre-define multiple risk level threshold ranges, including low risk, medium risk and high risk threshold intervals, expressed as: [R low ,R medium ,R high ]; Risk level determination: Based on the calculated current health risk level P(R t |y(t),X(t)), the user's health risk is divided into corresponding levels, expressed as: Low risk level: P(R t |y(t),X(t))≤R low ; Medium risk level: R low <P(R t |y(t),X(t))≤R medium ; High risk level: P(R t |y(t),X(t))>R medium .

9. The AI-based abnormal health event prediction system according to claim 8, characterized in that: The health management recommendations and interventions include: Recommended rule setting: According to different health risk levels, a rule base of corresponding health management suggestions and intervention measures is pre-set, including lifestyle adjustments, preventive measures and medical advice; Recommendation generation: Based on the user's current health risk level, the corresponding rules in the recommendation rule library are called to generate personalized health management recommendations and intervention measures, including: Low-risk level recommendations: Generate lifestyle adjustment recommendations focused on maintaining health, including a balanced diet, moderate exercise, and good sleep; Medium-risk level recommendations: Generate recommendations for preventive measures and enhanced health management, including regular physical examinations, increased exercise, weight control, and monitoring of blood pressure or blood sugar; High-risk level recommendations: Generate urgent medical recommendations and key health monitoring measures, including immediate medical attention, adjustment of medication regimens, medical examinations and follow-up.

10. The AI-based abnormal health event prediction system according to claim 1, characterized in that: The feedback and optimization module includes: Feedback information reception: receiving user feedback information, including changes in the user's health status, the effects of adopting health management suggestions, the user's self-perceived health improvements, and health data provided by the user; Model optimization: Based on the received user feedback information, the health trend prediction model is optimized using an incremental learning algorithm, including: Weight adjustment: Among them, w old and w new Respectively represent the model weights before and after the update, η is the learning rate, is the gradient of the loss function L with respect to the weight; Parameter update: Among them, θ old and θ new Respectively represent the model parameters before and after the update, γ is the parameter learning rate, is the partial derivative of the loss function L with respect to the model parameters; Algorithm modification: y new (t) = f(X(t); θ new )+Δy; Among them, f(X(t);θ new ) is the optimized parameter θ new The recalculated health trend prediction result, Δy, is the adjustment amount based on user feedback information.

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