1061 results about "Chronic disease" patented technology

The invention discloses a chronic disease early detection method and system based on a multi-modal large model, and relates to the technical field of intelligent medical treatment and artificial intelligence, and the method comprises the steps: obtaining a multi-modal data stream of a target user in a target time window from a pathology database, and generating an original multi-modal data set; performing timestamp unification and numerical value standardization processing on the original multi-modal data set to obtain a time sequence feature sequence; inputting the time sequence feature sequence to the multi-modal large model to obtain an abnormal symptom feature; calculating the similarity between the abnormal symptom features and feature vectors of marked cases in a historical case library, and determining matched cases; a diagnosis result and a development process of the matched case are extracted, a disease risk level and a development trend corresponding to the original multi-modal data set are determined in combination with the medical knowledge graph, and a pathology assessment result is obtained; and generating an early warning signal containing the risk type and the intervention suggestion according to the pathological assessment result. By implementing the application, the accuracy of early detection of chronic diseases can be improved.

The invention discloses an accurate health risk early warning analysis system and method based on multi-modal medical data fusion. The system comprises a multi-source data acquisition module, a preprocessing module, a dynamic fusion module, a risk assessment module, an interpretability module and a dynamic early warning module. According to the method, multi-modal data are collected, feature vectors are generated through preprocessing and cross-modal fusion, a comprehensive health risk index is calculated through a double-flow model (time sequence LSTM + static GNN), abnormal association is analyzed in combination with causal reasoning, a threshold value is dynamically adjusted, grading early warning is triggered, and finally the model is optimized through reinforcement learning. According to the scheme, deep fusion and dynamic evaluation of multi-modal data are achieved, the accuracy, timeliness and interpretability of risk early warning are improved, the method is suitable for scenes such as chronic disease management and intensive care, and powerful support is provided for clinical decision making.

The invention discloses a knowledge and data fused chronic disease collaborative management method and system, and relates to the technical field of medical management. Comprising the following steps: generating a feature data set, processing the feature data set by using a feature gain modeling method, and generating an enhanced feature data set; dynamically generating a chronic disease comprehensive knowledge graph, performing node supplementation and weight optimization on the knowledge graph, and generating a specific knowledge sub-graph of the patient; carrying out multi-time sequence prediction by utilizing the specific knowledge sub-graph, and carrying out short-term and long-term trend prediction; a patient health track model is constructed through the state transition probability in combination with the prediction result; according to the dynamic risk labeling result of the health track model, the health state of the patient is comprehensively evaluated in combination with the specific knowledge sub-graph, and personalized management suggestions are generated according to the evaluation result. According to the collaborative management platform, cooperation between different medical institutions and medical staff can be promoted, information sharing and resource integration are realized, and the overall level of medical services is improved.

The invention discloses a patient medication risk early warning method, and relates to the technical field of medication risk evaluation. The method comprises the following steps: acquiring multi-dimensional data such as age, past medical history, allergy history, clinical examination data and prescription data of a patient, extracting chronic disease diagnosis information from the multi-dimensional data, and carrying out standardized coding to generate a common disease combination tag; calling a preset disease weight system, and calculating a common disease severity score in combination with an age factor; extracting metabolism-related indexes to construct a drug metabolism capability feature vector of the patient; mapping the drug combination, the co-disease tag and the metabolic feature vector to a clinical drug knowledge base to generate a comprehensive conflict detection result; and generating corresponding early warning information based on the conflict detection result and a preset early warning rule base, wherein the rule base comprises a mapping relationship between the conflict type and the early warning level. According to the method, drug use risk early warning can be realized according to the multi-disease coexistence state of the patient, and the drug use safety is improved.

The invention relates to a digital chronic disease intelligent management platform based on an AI model and multi-dimensional data fusion, clinical diagnosis and treatment data, wearable equipment monitoring data, medication record data and environment monitoring data are acquired through a data acquisition module, and after standardized preprocessing is performed through a data fusion processing module, deep analysis is performed through an AI analysis module, and the data fusion processing module performs data fusion processing; in combination with medical knowledge of the knowledge base module, the intelligent decision-making module generates a personalized management scheme, and the personalized management scheme is implemented through the intervention execution module and the intelligent interaction module. Multi-dimensional health data are processed through an AI large model, a complex mode and an association relationship are automatically learned, and accurate disease prediction and risk assessment are realized; the pertinence of the scheme and the compliance of a patient are greatly improved; and real-time interaction and personalized guidance are provided, the participation degree and the self-management ability of the patient are effectively enhanced, and a benign health management cycle is formed.

The invention discloses an old people chronic disease risk prediction method and system based on a time sequence knowledge graph, and belongs to the technical field of medical health information, and the method comprises the following steps: S1, obtaining a health data set of an old people group; s2, on the basis of an ESOA-XGBoost algorithm, performing feature selection and sorting on each piece of health data in the health data set so as to determine correlation among variables, and generating a health data feature set; s3, constructing and training a time sequence knowledge graph prediction model including an STKGR-PR model and a T i PNN model, and predicting a relationship among the target entity, other entities and time sequence information through the time sequence knowledge graph prediction model; s4, predicting the old chronic disease risk of the old people group according to the trained time sequence knowledge graph prediction model; through the method, an elderly chronic disease risk prediction result with a good prediction effect and high accuracy can be obtained.

The invention relates to the technical field of medical data processing, in particular to a risk assessment system for chronic diseases. The system is used for performing physiological index analysis according to a medical physiological monitoring signal to generate physiological index data; performing individual physiological index weight empowerment on the physiological index data based on genomics data to obtain individual physiological index data; performing quantitative analysis on pathological influence factors of chronic disease types according to the electronic health record data and the individual physiological index data, and generating individual chronic disease influence factor quantitative data; performing composite interaction risk factor analysis on the individual chronic disease influence factors based on the quantitative data of the individual chronic disease influence factors to generate individual chronic disease composite interaction risk factor data; and performing individual chronic disease risk assessment based on the individual chronic disease composite interaction risk factor data to generate individual chronic disease risk assessment data. According to the invention, chronic disease health management and accurate evaluation are realized through collaborative evaluation work of chronic disease risks.

The invention discloses a chronic disease patient information management system and method based on intelligent AI, and relates to the technical field of information management. The information management system comprises a patient information management module, a data acquisition module, a data management module, a data analysis and evaluation module, a risk early warning module, a health management module and a remote medical service module. According to the chronic disease patient information management system and method based on the intelligent AI, learning and analysis are carried out based on comprehensive health data of the patient, so that accurate assessment of the health risk of the patient is realized, the current health data of the patient is integrated, and multi-dimensional information of medical history and living habits of the patient is combined, so that the assessment result is more comprehensive and accurate, and meanwhile, the assessment efficiency is improved. The health risk possibly faced by the patient in the future is predicted, early warning is given out in advance, sufficient time is provided for medical institutions to formulate intervention measures, potential health problems can be found and handled in time, and disease deterioration is prevented.

The invention provides a chronic disease risk assessment and intervention strategy generation system based on data analysis. According to the system, multi-source heterogeneous information including clinical examination, behavior records, environment data and the like is collected, key features are extracted through a data fusion technology, and time and space features of data are enhanced through a space-time weighted tensor decomposition method. And in combination with a causal reasoning technology, the system can accurately evaluate the chronic disease risk of an individual, eliminate confounding factors and provide more reliable risk prediction. In addition, the system dynamically generates a personalized intervention strategy through a reinforcement learning algorithm, adjusts intervention measures according to real-time health data, and ensures accurate chronic disease management. The method has an efficient risk prediction capability and a personalized intervention scheme, and is helpful for improving the accuracy and effect of chronic disease management.

The invention discloses a chronic disease patient personalized diet and exercise guidance system and method, and relates to the technical field of medical health information, and the system comprises a data sensing module which continuously collects the dynamic physiological data, behavior data and environment variable data of a patient through an intelligent sensing device, the dynamic physiological data comprises a heart rate time sequence, a step number time sequence and a blood glucose concentration time sequence monitored by the wearable device, and the behavior data comprises a medication operation record with a timestamp and a patient's daily symptom self-grading number. According to the personalized diet and exercise guidance system and method for the chronic disease patient, the time synchronization precision of multi-source data is effectively improved, the accuracy of medication compliance monitoring and physiological index correlation analysis is ensured, and by establishing the dynamic correlation model of the environment temperature and the human body metabolic rate, the accuracy of medication compliance monitoring and physiological index correlation analysis is improved. The timeliness and safety of clinical intervention are improved, and powerful support is provided for health management of chronic disease patients.

The invention relates to the technical field of medical aid decision making, in particular to a system and method for generating an individualized nursing scheme based on the comprehensive evaluation condition of the elderly. According to the method, various physiological time sequence data of the old people in a specific life scene are collected and the periodic fluctuation characteristics of the data are extracted, so that the subtle change trend of the daily rhythm of the old people can be accurately identified, the dynamic influence of the chronic disease progress on the physiological state is revealed by further comparing the rhythm deviation degree and continuity in different time periods, and the accuracy of the physiological state of the old people is improved. Then, in combination with the trend change rate and directionality thereof, an interaction mode between physiological changes and chronic disease indexes is described in continuous observation, so that the measurement of the coupling adaptation degree between individual physiological responses and chronic disease conditions is achieved, through normalized amplitude difference and matching interval screening, individual physical ability and chronic disease adaptation labels are accurately given, and the accuracy of the physical ability and chronic disease adaptation labels is improved. And further comprehensively measuring the adaptation priority of each intervention element in the individual in a multi-dimensional scoring mode, and constructing a nursing intervention combination oriented to a specific crowd state.

The invention relates to the technical field of health management, in particular to a personalized diet and exercise health management system fusing large model analysis ability, which comprises a multi-source data acquisition module, an integrated intelligent wearable equipment interface and the like, and is used for acquiring multi-dimensional health data; the health portrait modeling module fuses multi-modal data based on an improved Transform architecture, and outputs a dynamic portrait containing risk early warning and trend prediction; the personalized scheme generation module integrates the medical knowledge graph and the user gene features to generate a personalized health scheme; the scenarized recommendation engine matches diet and exercise resources in combination with a real-time scene; the closed-loop supervision optimization module implements scheme execution evaluation and adaptive adjustment through federated learning; and the risk early warning subsystem constructs an acute and chronic disease prediction model to realize risk early warning. According to the invention, precise health management is realized, the scheme effectiveness and the user experience are improved, the data security is guaranteed, and the intelligent development of health management is promoted.

The invention discloses a chronic disease follow-up visit and management platform driven by a large model, and relates to the technical field of medical information technology platforms. The system is applied to managing and tracking the follow-up visit process of chronic disease patients and providing health consultation and answers, and comprises a user interface module, a logic control module, a database module, a large model module and an auxiliary function module, and the user interface module is used for providing an interactive interface between a patient and a doctor. An intelligent and efficient system platform is constructed by integrating a large model technology, a voice recognition technology and a digital human technology, the system platform adopts a privatized large model, data security and privacy protection are ensured, medical institutions are allowed to autonomously optimize and control the application of the model, and on the whole, through an advanced technical means, the intelligent and efficient system platform has the advantages of being high in practicability and easy to popularize. Refining, individuation and high efficiency of chronic disease management are remarkably improved, and the purpose is to improve the health outcome and life quality of patients.

The invention discloses a hierarchical chronic disease management and referral system based on artificial intelligence, and belongs to the technical field of intelligent medical treatment, and the system comprises the steps: setting upstream and downstream referral paths composed of diagnosis and treatment institutions, binding responsible doctors corresponding to chronic diseases for the diagnosis and treatment institutions of each level, and forming a referral relation mapping table; predicting a chronic disease dynamic risk based on the multi-source and multi-modal data of the target crowd, classifying the target crowd, generating a chronic disease management suggestion for each type of crowd, and pushing the chronic disease management suggestion to a chronic disease doctor for contract signing management; generating referral suggestions based on the chronic disease dynamic risk changes of each type of people and pushing the referral suggestions to chronic disease doctors, and the chronic disease doctors determining the referral suggestions, setting referral information based on upstream and downstream referral paths and a referral relation mapping table and synchronously notifying target diagnosis and treatment institutions and patients; performance excitation is performed on whether a chronic disease doctor completes contract signing of a chronic disease patient, implementation of graded referral treatment and medical cost saving, so that the medical service precision can be improved, the diagnosis and treatment efficiency is improved, and medical cost throttling is promoted.

The invention discloses a multi-label chronic disease risk prediction device based on multi-modality and a graph neural network, and belongs to the technical field of intelligent medical treatment, and the device comprises a data processing unit which is used for obtaining electronic case data and carrying out multi-modality data screening, cleaning and preprocessing, wherein the multi-modal data comprises numerical value type inspection result data and text type inspection result data; the model construction unit is used for constructing a multi-label chronic disease risk prediction model comprising a multi-modal feature learning module, a multi-modal fusion module, a multi-disease correlation extraction module and a prediction module, and the application prediction unit is used for performing multi-label chronic disease risk prediction based on the constructed multi-label chronic disease risk prediction model. In this way, more effective features are mined from multi-source heterogeneous multi-modal data, and the correlation among chronic diseases is considered, so that on one hand, the risk of suffering from various chronic diseases is predicted more comprehensively, accurately and highly interpretably;

The invention discloses a knowledge enhancement-based neurogenic bladder personalized health management method and system, and relates to the technical field of chronic disease management, and the method comprises the steps: obtaining the illness state information of a neurogenic bladder patient, and obtaining text representation and knowledge graph representation according to the preprocessed illness state information; generating disease condition comprehensive information representation of the patient through splicing and fusion; constructing a chronic disease management scheme library of the neurogenic bladder, performing graph processing on the chronic disease management scheme library, constructing a health management scheme recommendation model based on a graph neural network, taking disease condition comprehensive information representation of the patient as model input, and generating a personalized health management scheme of the neurogenic bladder patient; and according to the personalized health management scheme, regularly performing scheme evaluation, and based on an evaluation score, judging the applicability of the personalized health management scheme. According to the method, the condition characterization of the patient is enhanced by means of massive information knowledge of the knowledge graph, and intelligent recommendation and construction of personalized schemes of chronic diseases of neurogenic bladder patients are enriched.

The embodiment of the invention provides a method and system for generating medical suggestions based on multi-modal data fusion, and the method comprises the steps: integrating a medical image, a physical examination report and dynamic physiological parameters of a patient through a multi-source data fusion module, generating a multi-modal data set, and synchronously inputting the multi-modal data set into a hybrid reasoning module and a dynamic knowledge graph engine. And the dynamic knowledge graph engine accurately recall a target diagnosis and treatment guide associated with the current multi-modal data set. The rule reasoning sub-module generates a first diagnosis suggestion containing a diagnosis conclusion, a treatment scheme and an evidence level based on a guide structured rule, and meanwhile, the neural network reasoning sub-module analyzes a multi-modal data set by relying on a triple topological structure and an edge weight; and generating a second diagnosis suggestion comprising the disease risk probability, the differentiated treatment suggestion and the evidence source. And finally, the interactive output module fuses the two suggestions to generate a medical suggestion report covering the diagnosis basis, the evidence level and the treatment scheme, so that the diagnosis and treatment precision of chronic disease management and health risk assessment is remarkably improved.

The invention relates to the technical field of medical system management, and particularly provides an outpatient medical insurance chronic disease medical data processing system based on artificial intelligence NLP. The system comprises a knowledge graph construction component which is configured to integrate multi-source heterogeneous data such as medical records, medical insurance and signs, perform cleaning, normalization and structural processing on the data through an NLP technology, and construct a chronic disease medical data knowledge graph; the diagnosis and treatment scheme matching component is configured to perform semantic analysis on the medical insurance policy document and the diagnosis and treatment guide through an NLP technology, and match a diagnosis and treatment scheme and a medical insurance reimbursement path in combination with the actual illness state and the medical insurance reimbursement record of the patient; the analysis and early warning assembly is configured to analyze the long-term health data of the chronic disease patient, identify potential health risks and development trends, and provide personalized health management suggestions and early warning prompts. According to the method, multi-source data integration and knowledge graph construction are achieved through the NLP technology, and the problem of data heterogeneity and the semantic association requirement are solved.

The invention relates to the technical field of medical early warning, and discloses a graded early warning system based on multi-parameter vital sign detection. According to the system, real-time physiological parameters such as the heart rate, the blood pressure, the oxyhemoglobin saturation and the body temperature of a patient are collected through vital sign monitoring equipment; and inputting the parameters into a feature extraction network, generating a multi-dimensional physiological feature vector, and constructing a dynamic risk assessment matrix containing physiological state change trends of different time windows according to the multi-dimensional physiological feature vector. Dividing risk grade intervals according to a preset grading early warning threshold value, adjusting the intervals by adopting a self-adaptive weight distribution strategy, and generating a comprehensive risk score; and when the score exceeds the preset early warning trigger line, activating a corresponding early warning response mechanism. The system can realize comprehensive dynamic assessment of the physiological status of the patient, is suitable for emergency treatment, intensive care and chronic disease nursing scenes, and meets the clinical health risk monitoring and early warning requirements.

The invention discloses a tongue picture characteristic change rule analysis method based on the rehabilitation process of a chronic disease patient, and relates to the technical field of health big data analysis, and the method comprises the steps of tongue picture data standardized collection, tongue picture data recording system establishment, tongue picture characteristic data analysis, result visualization and report generation. According to the invention, through a standardized tongue picture data acquisition mechanism, the quality and consistency of the tongue picture data are ensured, and through controlling illumination conditions, background colors, diet restrictions and posture requirements, the influence of external factors on the tongue picture data is reduced, and the accuracy and reliability of the data are improved. By comprehensively evaluating definition, noisy points, contrast ratio and color accuracy verification parameters, it is ensured that collected tongue picture data reaches a preset quality standard, and a high-quality data basis is provided for subsequent analysis; meanwhile, a comprehensive tongue picture data recording system is established, and tongue picture data are effectively stored and managed.

The invention provides a chronic disease co-patient accurate medication real-time management system based on an AI large model, and the system comprises accurate medication guided by the AI large model, through the mathematical modeling and simulation technology, the related information of patients, medicines and diseases is integrated, and accurate medication is guided. According to the method, the administration scheme can be formulated more accurately, the curative effect is improved, side effects are reduced, a patient medication report is provided for physicians and pharmacists, the physicians and pharmacists can dynamically master patient medication information in real time, reference is provided for the physicians to formulate the next medication scheme, and the treatment effect on chronic disease patients is improved.

The invention relates to the technical field of medical treatment, in particular to an intracranial aneurysm rupture risk assessment and management system based on an internet hospital, which comprises a patient end module, a patient end module, a management end module and a management end module, and the patient end module uploads medical history data and performs automatic filing and labeling processing on basic health data through a system standardization questionnaire and a multi-modal data acquisition mechanism; according to the method, the consistency and comparability of the health data are realized through vectorization coding and standardization processing; a blind deconvolution technology is adopted to improve the definition of a cerebrovascular image, and risk assessment is optimized in combination with the image and clinical features; privacy is protected through a self-adaptive watermarking technology, and data security and compliance are ensured in combination with double encryption and de-identification processing; the system timely identifies risks and pushes personalized health intervention through dynamic monitoring and intelligent early warning, and the accuracy and initiative of chronic disease management are improved. In addition, the block chain technology ensures data traceability and tampering prevention, and the doctor-patient trust is enhanced.

The invention relates to the technical field of artificial intelligence, in particular to an old-age care chronic disease management method, system and device based on artificial intelligence and a medium. The method comprises the following steps: firstly, acquiring historical medical data of a patient, extracting features by using a pre-trained deep neural network, and performing feature analysis through an old-age disease knowledge graph; then, based on the health feature data, a time sequence prediction model is adopted to evaluate the chronic disease risk; combining a risk assessment result with physiological index data collected in real time to predict a disease development trend; a personalized treatment scheme is formulated according to the illness state evaluation and prediction result; the scheme is dynamically adjusted by analyzing treatment compliance data of the patient; and finally generating a scheme including medication reminding, complication prevention and lifestyle guidance. Through mining of historical data, accuracy of risk assessment is ensured by using time sequence prediction, individuation of a treatment scheme is ensured by using reinforcement learning, and adaptability of a management scheme is improved through dynamic adjustment.

The invention provides an intelligent management system for chronic diseases and common diseases of old people. The intelligent management system comprises an old people comprehensive evaluation module, a multidisciplinary collaborative diagnosis and treatment module, a multi-medication risk prediction management module, an intelligent chronic disease follow-up diagnosis management module and a health science popularization knowledge base module, the senile comprehensive assessment module inputs the personal chronic disease map of the patient into a risk factor prediction model for prediction, and outputs a comprehensive assessment result; the multidisciplinary cooperative diagnosis and treatment module is used for the first diagnosis / receiving diagnosis doctor to coordinate multidisciplinary doctors to perform cooperative diagnosis and treatment; the multi-medication risk prediction management module is used for giving comprehensive diagnosis, multi-medication management and risk prediction to the patient through collaborative diagnosis and treatment; the chronic disease intelligent follow-up diagnosis management module carries out follow-up visit tracking, health risk prediction and review prompting on the patient; and the health science knowledge base module matches and pushes corresponding health education knowledge. The accuracy and timeliness of senile chronic disease co-disease risk assessment prediction and management are improved, and a good auxiliary effect is achieved on improvement of the life quality of patients.

The invention discloses a chronic disease intervention safety detection system and method based on large model multi-agent cooperation, and relates to the technical field of natural language processing and multi-agent cooperation in the medical health field. A standardized interface and an adaptive cleaning algorithm are adopted to realize data desensitization, missing value filling and format unification, and a dynamically updated patient health portrait is constructed; complex tasks are decomposed based on a chain reasoning technology, special tools such as a drug interaction detection tool and a nutrition gap calculation engine are developed, and a clinical knowledge base is integrated for parallel analysis; suggestion conflicts are eliminated through a multi-agent debate mechanism, the priority is calculated in combination with a weight rule, manual auditing is triggered to process low-confidence disputes, and finally a structured health report containing medication adjustment, diet optimization and behavior intervention is generated. The limitation of a traditional method in data integration, cross-domain reasoning and conflict resolution is solved.

The invention relates to the field of chronic disease early warning, and discloses a chronic disease early warning intervention system based on artificial intelligence, and the system comprises a data collection module which is used for collecting multi-modal data, and the multi-modal data comprises gene detection data, proteomics data, microbiomics data, environment data and social economic data; the pre-processing module is used for pre-processing the multi-modal data to obtain basic data, and the pre-processing comprises noise removal, abnormal value and missing value processing and targeted data cleaning and feature extraction operation on the data of different modals; and a conversion fusion module. Through cooperative operation of all the modules, a complete and automatic chronic disease early warning intervention process is formed, an organic whole is formed from data acquisition, risk assessment and intervention measure formulation and implementation, and the chronic disease risk whole process management and control are achieved, so that the chronic disease prevention and treatment efficiency and effect are improved.

The invention discloses a coronary artery calcification early warning system for type 2 diabetes patients, and relates to the technical field of medical detection. A data acquisition module is used for acquiring continuous physiological parameter data of a user; the risk modeling module is combined with coronary artery calcification evolution characteristics in historical clinical samples to construct a multi-parameter dynamic association model; an index weight calculation unit generates a risk influence factor vector based on a sensitivity analysis result of the physiological indexes on risk prediction; the machine learning analysis module performs iterative training on the prediction model by adopting an integrated learning algorithm, and performs prediction updating by utilizing a risk influence factor vector; the early warning trigger module dynamically generates a graded early warning signal according to the grading trend and a set threshold value; the weak item positioning module carries out contribution degree analysis and anomaly recognition on the key risk indexes and automatically generates personalized intervention suggestions; according to the invention, early recognition and dynamic early warning of coronary artery calcification progress can be realized, and the method is suitable for intelligent early warning management scenes of chronic disease cardiovascular risks.

The invention discloses an AI-driven health question-answering method and system oriented to individual health management, relates to the field of medical AI, is applied to a health question-answering system, and constructs a chronic disease knowledge database according to medical data obtained through screening and crawling; adopting a first training process to obtain a trained base model; designing an Agent agent; obtaining an input question of a user; the Agent agent automatically calls the chronic disease knowledge database according to an input question, and performs networking search and reasoning answering to generate a first answer; and correcting the first answer through the guidance of the predefined template and the input cue word, and outputting the corrected first answer, thereby achieving the technical effects of improving the medical data quality and utilization efficiency, optimizing the model training process and performance, and enhancing the model interaction capability and user experience.

The invention relates to the technical field of occupational health management, and particularly discloses a method for health assessment and management of occupational crowds, which comprises the steps of S1, health management service planning, S2, health management service principles, S3, health management service crowd classification, S4, health management service modes and S5, health management service contents. S6, building a health management information system; and S7, evaluating and feeding back effects. Through health monitoring, early intervention and continuous management, occurrence and development of chronic diseases, cardiovascular diseases and diabetes mellitus are effectively prevented and delayed, the health level of the whole employees is improved, after a health management project is implemented, the health conditions of the employees are improved, the conditions of false illness and absence caused by the diseases are reduced, and the safety of the employees is improved. The system can be used for preventing and controlling diseases, particularly preventing and controlling chronic diseases, the frequency of absence due to diseases is remarkably reduced, and resource waste caused by excessive medical treatment, repeated physical examination and the like is reduced through precise health assessment and personalized intervention measures.

The invention discloses an interpretability visualization method and system based on chronic disease dynamic prediction, and belongs to the technical field of intelligent medical treatment, and the method comprises the steps: carrying out the distributed processing, standardized storage, distributed verification and undersampling of medical data, and obtaining a balanced data set comprising effective samples; extracting a disease tag of a previous time node from each patient as a feature, and performing feature importance evaluation by using a tree model to screen out an important feature subset; inputting the effective samples into a chronic disease prediction model, calculating the marginal contribution of each important feature, and visualizing the marginal contribution into a force diagram for analyzing single sample prediction logic; and calculating the interaction importance between every two features, screening feature interaction pairs, visualizing the feature interaction pairs into text rules and corresponding influence factors, and constructing the interaction pairs into new features for retraining the chronic disease prediction model. According to the method, the interpretability of the model in a chronic disease prediction technology can be enhanced, and the transparency and traceability of decision logic of a complex model are realized.