550 results about "Chronic disease" patented technology

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 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 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 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 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 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 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 a chronic disease risk prediction method and system fusing a knowledge graph and a large language model, and the method comprises the following steps: obtaining a natural language problem related to a chronic disease, and carrying out the semantic analysis; according to the analysis content, hypothetical questions and answers related to chronic diseases are generated through a large language model, and key entities are extracted; mapping the key entities to corresponding nodes in a medical knowledge graph, exploring a semantic path and a causal relationship between the key entities, and constructing an inference chain pointing to potential disease risks from acquired information; introducing a fragment granularity sensing mechanism, performing fine granularity analysis on each fragment in the reasoning chain, and rearranging and optimizing a link sequence; and based on the optimized inference chain, converting the question and answer result into a structured diagnosis result for visual display. According to the method, the whole process from question asking to answer generation of the patient is optimized, the efficiency and accuracy of chronic disease risk prediction are effectively improved, and meanwhile, personalized health management service is provided for the patient.

The invention relates to the technical field of treatment of chronic diseases of old people, and discloses a metabolic characteristic-based medicine dosage optimization method for chronic diseases of old people. The method comprises the following steps: acquiring drug metabolism parameters (including a plasma concentration peak value, a half-life period curve and the like) and organ function data (including a hepatocyte metabolism rate, a glomerular filtration rate and the like) of a plurality of monitoring nodes, arranging the drug metabolism parameters and the organ function data into a time sequence input vector, and extracting a dynamic feature vector by using a time convolution network and an adaptive filter network; performing feature crossing, pharmacokinetic constraint correction and feature enhancement processing, performing fusion to generate a joint feature vector, inputting the joint feature vector into a dose decision model to obtain an adjustment coefficient, and generating a drug dose interval with a safety threshold in combination with a historical drug use record. According to the method, multi-dimensional data integration and dynamic modeling are realized, the accuracy and safety of chronic disease medication of old people are improved, and the method is suitable for individualized treatment.

The invention discloses a full-cycle path chronic disease management system and method based on artificial intelligence, and belongs to the technical field of intelligent medical treatment, and the method comprises the steps: generating a model result based on an artificial intelligence chronic disease risk prediction model and a prescription through an active diagnosis and treatment module, and carrying out the active recognition, intervention and management of a target group; the personalized diagnosis and treatment module is used for providing refined follow-up visit, prescription and screening services according to health states and risk characteristics of different individuals; and semantic search, index generality identification and multi-source data integration capabilities of clinical data are provided through an intelligent data governance and decision support module. According to the invention, an intelligent medical new mode of active, personalized and intelligent chronic disease management is constructed, clinical decision is assisted, the management efficiency is improved, the chronic disease management level is improved, reasonable flow of medical resources is promoted, and chronic disease prevention, management and referral are promoted to develop towards the full-life-cycle management direction.

The invention discloses an individual health risk dynamic assessment system based on multi-modal biological feature fusion, and relates to the technical field of medical health information processing. The method comprises the following steps: constructing a high / low-dimension data combination rule; dynamically adjusting the feature fusion weight according to the clinical feature importance weight and the medical efficiency value calculated by the detection time interval; a cost difference value formula is introduced to quantify the economical efficiency of combined evaluation and independent evaluation; calculating a health risk comprehensive coefficient of the monitoring scene by combining the health data credibility score and the index type / amplitude / duration weighted risk value; the risk level is rechecked through expert assessment or a secondary model, so that the assessment accuracy is ensured; updating the individual credibility score according to the evaluation result; and for the result exceeding the confidence interval, classifying and optimizing a feature fusion algorithm, a model parameter or a data combination strategy. The system realizes accurate, economic and real-time individual health risk dynamic assessment, and is suitable for scenes of chronic disease management, health intervention and the like.

The invention discloses a chronic disease management AI recommendation anti-illusion method and system based on a knowledge graph, and relates to the technical field of medical health artificial intelligence, and the method comprises the steps: obtaining chronic disease medical data of a patient, extracting a feature vector, and constructing a multi-modal health trajectory vector; constructing a chronic disease tag vector and a directional causal knowledge graph; executing map path search to obtain candidate treatment paths; constructing structured cue words, and inputting the structured cue words into the medical large language model to generate candidate schemes; and calculating a comprehensive illusion score to carry out anti-illusion judgment and correction, and outputting a credible personalized treatment recommendation. By constructing a directional causal knowledge graph, the causal reasonability and semantic consistency of treatment path retrieval are enhanced, and the matching precision of candidate paths and patient states is improved; by calculating the comprehensive illusion score and introducing the four-dimensional score item for anti-illusion judgment, the credibility of the output content of the large language model is improved, and the availability of AI recommendation in clinical aid decision making is guaranteed.

The invention relates to the technical field of reinforcement learning, and discloses a chronic disease intervention scheme generation method and system based on reinforcement learning and multi-modal data, and the method comprises the steps: obtaining the multi-modal data of a patient, the multi-modal data at least comprising electronic medical record data, voice data, image data, text data and physiological time sequence data; performing feature extraction on the multi-modal data to obtain a multi-modal feature vector; on the basis of the multi-modal feature vectors, health state vectors are constructed, and the health state vectors at least comprise a physiological risk score, a treatment compliance score and a lifestyle health degree score; designing a reward function according to the dynamic change of the health state vector; optimizing the strategy network in a predefined intervention action space according to the reward function by utilizing a reinforcement learning algorithm so as to output an optimal intervention action; and converting the optimal intervention action into personalized natural language interaction content through a generative AI model. According to the invention, the efficiency, precision and patient compliance of chronic disease management can be significantly improved.

The invention relates to the technical field of medical health monitoring, and particularly discloses a chronic disease patient physical examination data and complication nursing monitoring system based on the Internet of Things, multi-dimensional physical examination data such as vital signs and blood biochemical indexes of a patient are collected in real time through a multi-source sensing equipment network, and an individualized data stream is constructed; extracting parameter collaborative change characteristics by adopting multi-time scale analysis, and constructing a dynamic physiological characteristic spectrum; identifying the abnormal offset by calculating the deviation degree of the feature vector distribution and the reference distribution; based on intelligent matching of the spatial distribution features of the feature vectors and a clinical case library, generating structured early warning information including risk assessment and personalized nursing schemes; and finally, visual display and preventive nursing intervention of the complication risk evolution process are realized.

The invention discloses a traditional Chinese medicine chronic disease dialectical treatment optimization method based on a graph neural network, and the method comprises the following steps: S1, collecting electronic medical record text data and traditional Chinese medicine knowledge data of a patient, and constructing an initial dialectical graph; s2, obtaining an initial feature vector of a node; s3, performing multi-order spiral perception processing by using a boa convolution unit, constructing a spiral adjacent path for each target node according to a structure depth and a relation direction, and introducing a structure position coding mode to perform sequential perception modeling on a node neighborhood relation to generate an adjacent feature matrix; s4, constructing a syndrome semantic tension matrix; s5, inputting the adjacency feature matrix and the syndrome semantic tension matrix into a GATv2 model to obtain a final node feature vector; and S6, outputting a syndrome type prediction result corresponding to the patient by using the final feature vector of the node, and generating prescription and drug path recommendation. According to the method, the GATv2 model and the boa convolution unit of structure perception are combined, so that traditional Chinese medicine chronic disease dialectical reasoning and drug recommendation are realized.

The invention relates to an intelligent decision-making system for nutrition metabolism collaborative management of elderly chronic disease patients, in particular to the field of nutrition metabolism collaborative management of elderly chronic disease, which is characterized in that drug molecule characteristics and nutrient metabolism paths are dynamically integrated through a multi-modal knowledge graph, and a cross-domain associated three-dimensional knowledge network is constructed; based on a reinforcement learning real-time optimization rule confidence threshold value, the early warning sensitivity is adaptively adjusted according to the degree that the metabolic index of the patient deviates from the safety interval; the streaming conflict detection engine accurately identifies the potential risk of asynchronously input medication and diet data, and triggers graded early warning through space-time alignment and sub-graph matching; the closed-loop evolution mechanism fuses patient compliance feedback and blood potassium change trend, drives the taboo rule base to continuously and autonomously evolve under the constraint of renal function layering, and realizes personalized risk prevention and control and metabolic state collaborative optimization.

The invention belongs to the technical field of medical health monitoring, and provides a human health prediction method and system based on facial video physiological signal detection, and the method comprises the steps: collecting a facial video stream, and extracting time sequence physiological signals such as heart rate, HRV, respiratory rate and the like through an rPPG algorithm; constructing a graph database individual health portrait in combination with multi-scale time sequence alignment; adopting a dynamic threshold algorithm to detect instantaneous anomaly, and fusing nonlinear dynamics and waveform morphological characteristics to quantify anomaly; constructing a hybrid model, extracting space-time and high-order features, and modeling multi-parameter interaction; a prediction result is dynamically corrected based on a Bayesian algorithm, and health risk layering is realized through clustering; and outputting the visual health report. Through non-contact monitoring, multi-modal fusion and edge-cloud collaborative architecture, the problems that traditional equipment is low in compliance, non-contact technology is insufficient in precision and prediction is shallow are solved, dynamic health prediction and closed-loop management are achieved, and the system is suitable for scenes such as remote monitoring and chronic disease screening.

The invention is a system for continuous monitoring of respiratory and gastric functions using wearable devices. Each device includes a three-axis accelerometer and optionally a gyroscopic sensor. They feature wireless communication for data transmission and means for attachment to the skin. A computation device analyzes the data, using a machine learning model to detect coughing, swallowing and respiration. The method involves placing sensors on the skin of the cricoid region and epigastric region, filtering and transmitting data, training the model, and plotting coughing frequency. This system enhances detection accuracy and facilitates early medical interventions, benefiting chronic condition management and postoperative care.

The invention discloses a risk assessment stabilization method for senile chronic disease detection, and relates to the technical field of medical data analysis, and the method comprises the steps: carrying out federal dynamic time warping processing on encrypted patient data streams of a plurality of medical institutions, generating a joint feature space mapping matrix, and obtaining a standard feature tensor through homomorphic encryption; inputting the standard feature tensor into a dynamic medical knowledge graph construction module, and generating a knowledge graph embedding matrix through a space-time sensitivity enhanced cross-modal attention mechanism; and fusing the standard feature tensor and the knowledge graph embedding matrix, constructing a dynamic hypergraph structure, executing dual-channel hypergraph convolution calculation, and outputting a patient-knowledge joint embedding matrix. According to the method, in the construction of the dynamic knowledge graph, the time sensitivity weight of the entity relationship is quantified through the exponential decay function, and the time-space enhanced embedded matrix is generated in combination with the cross-modal attention mechanism, so that the time sequence discrimination of the concurrent disease association strength is improved.

The invention relates to the technical field of artificial intelligence, and discloses a system of a nutrition recipe recommendation system based on artificial intelligence in chronic disease intervention management and an application method. Comprising a data acquisition module, a data preprocessing and feature engineering module, a knowledge base management module, a personalized nutritional requirement modeling module, a recipe generation and optimization module, a user interface module and a model training and updating module which are in communication connection through a network. According to the method, personalized health data such as physiological indexes, chronic disease characteristics, diet preference, allergy and intolerance information of a user are collected in multiple dimensions, a nonlinear relation between the characteristics of the user and nutritional requirements is deeply mined in combination with a deep learning model, and an accurate personalized nutritional requirement model is generated according to specific illness conditions and body indexes of different chronic disease patients; therefore, recipe recommendation conforming to individual differences is provided, and the risk of aggravating the illness state due to improper diet is reduced.

The invention discloses a chronic disease screening return visit method and system, and relates to the technical field of health management, and the method comprises the steps: obtaining the basic information of a to-be-screened person, and constructing a screening file; generating a screening voucher containing an identity label; parallel data acquisition posts are set, personnel hold vouchers to autonomously select posts, and the posts scan identification associated archives and input multi-dimensional data to a central database in real time to form an associated data set; when the data set contains a preset basic data item, calling a risk scoring model to calculate a chronic disease risk score; and generating a differential detection strategy based on a comparison result of the score and a preset threshold value. According to the method, through parallel post setting and data real-time integration, the problems of post congestion and low efficiency of data integration in a traditional screening process are solved, intelligent risk assessment and detection strategy distribution of to-be-screened personnel are realized, the chronic disease screening efficiency and the resource utilization rate are improved, and a systematized data management scheme is provided for early prevention and control of chronic diseases.

The invention, which relates to the technical field of data processing, discloses an intelligent perception-driven personalized health care data management system comprising a data acquisition unit, an index fusion and modeling unit, a health state evaluation unit and a personalized feedback unit. According to the personalized health care data management system driven by intelligent perception, the technical defect of false alarm caused by a static health threshold under the interference of environment sudden change or group behaviors is overcome through the synergistic effect of the three-dimensional health risk parameters and a dynamic safety threshold boundary mechanism; and when the environmental index abnormally fluctuates, the system automatically refreshes the security boundary, and a hierarchical response mechanism is triggered in combination with the risk time length, so that the chronic disease deterioration trend prediction accuracy is greatly improved.

The invention relates to the technical field of medical artificial intelligence and chronic kidney disease management crossing, in particular to a nonlinear algorithm system for precise chronic disease management of chronic kidney diseases. Through deep coupling of algorithms and in combination with two exclusive quantification formulas, transformation of CKD chronic disease management from standardized follow-up visit to precise and dynamic algorithm driving is realized, and the core pain points of large staging evaluation deviation, progress pre-judgment lag, poor intervention scheme adaptability, follow-up visit strategy homogenization and the like in traditional CKD management are solved. The method provides full-cycle decision support for nephrology doctors in the three-A hospital, customizes an individualized chronic disease management path for CKD patients, is suitable for CKD 1-5 period full-staging patients, and can be widely applied to CKD special management scenes of chronic disease diagnosis and treatment centers in the nephrology department of the three-A hospital, chronic disease management outpatient clinics in special nephropathy hospitals and high-end health management institutions.

The invention discloses a chronic disease early warning intervention system based on artificial intelligence, and relates to the technical field of chronic disease early warning, and the technical scheme is characterized in that multi-modal data of a patient is collected and preprocessed, and a feature vector is generated; performing feature extraction on the feature vector to generate a fusion feature vector; predicting a risk level by using a multi-layer perceptron classifier, and estimating a chronic disease occurrence probability; checking and correcting the risk prediction result according to the expert knowledge base; constructing a chronic disease association map to calculate the joint probability of complications, and generating a personalized intervention scheme of the patient; and performing feedback optimization on the intervention scheme through a reinforcement learning algorithm. According to the system and the method, multi-modal data and expert knowledge are fused, and an artificial intelligence technology is combined, so that chronic disease early-stage accurate early warning, cross-disease complication risk assessment and personalized intervention scheme generation are realized, better health management services can be brought to chronic disease patients, and the burden of chronic diseases on personal health and society is reduced.

The invention discloses a chronic disease risk prediction and dynamic management system based on multi-modal data fusion, and relates to the technical field of chronic disease management, the chronic disease risk prediction and dynamic management system comprises a data acquisition layer, a data fusion processing layer, a risk prediction management layer and an application service layer, the data fusion processing layer performs preprocessing, feature extraction and fusion analysis, the risk prediction management layer constructs a risk prediction model according to result analysis, and the application service layer provides a risk prediction result, a personalized management scheme and an interactive interface service for a user. Through the arrangement of the data acquisition layer, the data fusion processing layer and the risk prediction management layer, 'symptom-physiology-image 'full-dimensional data is covered, more comprehensive health state evaluation is supported, dynamic intelligent prediction can be carried out, diet, exercise and medication suggestions are automatically adjusted according to patient execution feedback and the latest prediction result, and the health state evaluation efficiency is improved. And a self-adaptive management cycle is formed.

The invention discloses a personalized recipe recommendation system, a recommendation method and an interaction system, and relates to the technical field of diet recommendation, the system comprises a user data collection module, a food image recognition module and a personalized recommendation module, the user data collection module is used for obtaining health data and diet preference data of a user, the health data comprises basic information, health conditions and exercise habits, the basic information comprises regions, the health conditions comprise allergen lists, chronic diseases and diet targets, and the food image recognition module is used for obtaining food photos uploaded by a user, recognizing the food photos to obtain food recognition results and sending the food recognition results to the user. The food identification result comprises a food type corresponding to the food photo, and the personalized recommendation module is used for determining a recommended recipe recommended to the user based on the health data, the diet preference data and the food identification result. By considering more comprehensive influence factors, a more suitable and personalized recipe can be recommended to the user.

The invention discloses a bronchial asthma symptom intelligent monitoring method and system based on multi-source data, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining multi-mode asthma data after time-space alignment; performing weighted fusion on the western medicine index, the environment index, the tongue condition index and the pulse condition index by using a preset pathogenesis mapping rule to generate an asthma pathology state vector; inputting the asthma pathology state vector into an emergency response channel, analyzing abnormal change of short-term peak flow velocity, synchronously inputting into a chronic evolution channel to track long-term change of tongue condition and pulse condition, and generating emergency response integral and syndrome integral; and superposing the emergency response integral and the syndrome integral on a time axis, and triggering risk assessment when the emergency response integral and the syndrome integral meet the time condition coincidence and the peak flow rate declines to reach an early warning threshold value. According to the method, asthma pathology state vectors are respectively input into an emergency response channel and a chronic evolution channel, so that synchronous monitoring and dynamic modeling of acute attack and chronic disease courses are realized.

The invention relates to a chronic disease risk automatic early warning method based on multi-modal data analysis, and belongs to the technical field of intelligent medical treatment and health management. The method comprises the following steps: acquiring multi-modal health data of a user in real time through a multi-modal data acquisition module, and performing feature extraction to obtain individual multi-dimensional health features; based on the features, a convolutional neural network and a graph neural network are called to be combined with the chronic disease type to construct a chronic disease risk prediction model, and potential chronic disease risks are judged through leaving-one verification; analyzing a risk level according to the risk result and generating early warning information; and carrying out risk detection based on the evaluation index, and if the risk is higher than a model set threshold, sending a risk early warning signal through the terminal device. Accurate assessment and personalized early warning of chronic disease risks are realized.

Provided herein are systems and methods for identifying a disease or disorder of a patient, identifying if a patient is likely to respond to a treatment for the disease or disorder, and / or predicting the clinical outcome of the disease or disorder of a patient. Systems and methods described herein may be directed to patients with different chronic conditions, inflammatory conditions, and / or autoimmune conditions. Systems and methods described herein may be directed to patients with a dermatological condition.

The invention relates to the technical field of chronic disease health management, and discloses a chronic disease health management method based on data analysis, and the method comprises the following steps: S1, obtaining medical records, wearable device data, laboratory results, self-reporting information and environment data of a patient through a multi-source collection gateway; and S2, performing desensitization processing and feature extraction on the data under a federated learning framework, and generating a multi-modal patient portrait containing time sequence, spatial features and biomarkers. According to the chronic disease health management method and system based on data analysis, through desensitization processing under a federated learning framework, on the premise of protecting patient data privacy, medical records, wearable device data, environment data and other multi-source information are effectively integrated, a comprehensive multi-modal patient portrait is formed, and the patient experience is improved. The problem that data are mutually separated in a traditional management mode is solved, and comprehensive data support is provided for subsequent risk prediction and intervention decision making.