784 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 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 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 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 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 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 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 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 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.

The invention belongs to the technical field of intelligent medical treatment, and discloses a chronic disease information management system based on a behavior interaction model. Comprising the following steps: acquiring background information, internal motivation and cognitive evaluation; based on the background information, a health education content library is constructed, and health education content is pushed in combination with cognitive evaluation; fusing the background information and the internal motivation to generate a multi-dimensional treatment scheme matrix; acquiring willingness information of a patient, performing quantitative evaluation on acceptance degrees of different treatment schemes, and screening out an optimal treatment scheme; acquiring and analyzing real-time interaction data, and dynamically formulating an emotion support strategy; patient health data are integrated, and the interaction effect is evaluated; according to the interaction effect, the health education content, the optimal treatment scheme and the emotion support strategy are intelligently optimized in sequence; behavior interaction is taken as the core, and accurate management of the whole life cycle of the chronic disease patient is realized, so that the health management effect of the patient is remarkably improved, and the complication risk is reduced.

The invention discloses a chronic disease health management large model construction method based on an uncertainty knowledge graph, and the method comprises the steps: generating a high-quality dialogue data set in the field of chronic disease health management through employing the uncertainty knowledge graph, and combining a knowledge graph retrieval and reasoning enhancement technology; and the reasoning precision and reliability of the large model in the field of chronic disease health management are improved. According to the method, firstly, a multi-agent cooperation framework is constructed, knowledge is extracted from a chronic disease health management uncertainty knowledge graph, a dialogue data set suitable for the field is generated, and field adaptability fine tuning is conducted on a basic model through a two-stage training strategy. When a user asks a question, the question is firstly analyzed, a relation path supporting answering is generated, and then a reasoning path is retrieved from the uncertainty knowledge graph according to the relation path. The reasoning paths are reordered and screened through a dynamic threshold screening mechanism of uncertainty perception, and finally, accurate and credible chronic disease health management suggestions are provided for users in combination with the screened reasoning paths.

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 health data processing, in particular to a cross-device health data fusion method, which comprises the following steps of monitoring a device connection state, identifying data missing and resending, calibrating multi-device time and performing data difference, calculating a device stability score to adjust confidence fusion data, extracting periodic health parameters and analyzing a change trend. And detecting the factor activation state and combining the sensitivity to obtain a risk score, and outputting a health processing result. According to the method, through combination of equipment connection state identification and data caching progress calculation, the precision of breakpoint data recovery is enhanced, a timestamp alignment and interpolation correction mechanism is adopted, continuous compensation of multi-source data is realized, the data weight is adjusted by using a stability score, the credibility of fused data is improved, and the reliability of the fused data is improved. In combination with behavior characteristic trend extraction and risk factor sensitivity determination, the accuracy and individualized distinguishing ability of chronic disease risk identification are optimized, and the individualized health management and disease prevention ability is enhanced.

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 provides a chronic disease risk early warning system and method based on artificial intelligence. The method comprises the following steps: acquiring historical monitoring information of a diabetic patient; determining development trends of different disease course stages through historical monitoring information, and performing trend evolution based on all the development trends to obtain evolution characteristics of each disease course stage; determining the risk contribution degree of each health index to the chronic disease risk according to the linear correlation among different health indexes, and determining a risk monitoring model of the target patient through all the risk contribution degrees; performing confidence adjustment on the chronic disease risk in the risk monitoring model according to each evolution feature, and further obtaining a confidence risk value of the current disease course stage of the target patient; and carrying out risk prompting on the target patient based on the confidence risk value. By adopting the scheme of the invention, real-time modeling can be carried out on the dynamic change of the course of disease of the patient under the background of massive health data.

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 tumor recurrence risk prediction method and system based on electronic medical record data, and relates to the field of electronic medical record data processing and analys.The historical electronic medical record data are processed in a structured mode, a recurrence risk mapping model is established, individualized recurrence risk assessment can be achieved based on multi-dimensional features, and the tumor recurrence risk prediction accuracy is improved. The subjective judgment error is obviously reduced; a risk level layering mechanism can automatically distinguish patients needing emergency intervention and conventional monitoring, excessive medical treatment or delayed treatment is avoided, and the method is particularly suitable for chronic diseases such as tumors needing long-term management; through periodic marker measurement and risk level feedback, closed-loop management of evaluation-intervention-re-evaluation is formed, and the requirement for continuous optimization of clinical diagnosis and treatment is met; in addition, through preprocessing, the preprocessed multi-dimensional features can be directly called subsequently, and repeated data cleaning work is avoided.

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 discloses a chronic disease evaluation system and method based on a weak AI medical large model platform, and particularly relates to the technical field of medical artificial intelligence. The system constructs a multi-modal input matrix and generates fusion features by collecting multi-modal data such as electronic health records, wearable device time sequence signals and medical images; calculating a risk index by using cooperative work of a cloud large model and an edge end lightweight model and combining a current state and a historical trend; an early warning threshold value is dynamically adjusted according to the disease type and historical risk data, and risk early warning is achieved; key features are extracted, similar cases are matched, and an interpretability report is generated; and updating edge end model parameters through federal learning to protect data privacy. The problems of data splitting, static assessment, resource dependence and the like in a traditional chronic disease assessment system are solved, comprehensive, dynamic and accurate assessment of chronic diseases is achieved, and the method has high clinical application value.

The invention belongs to the field of nano-enzyme preparation and biological application, and particularly relates to a preparation method and application of M2 type macrophage membrane coated FeMn diatomic nano-enzyme. The preparation method comprises the following steps: by taking nitrogen-doped carbon (BANT) as a carrier, loading Fe and Mn diatoms, synthesizing a novel nano material FeMnDA / BCNT diatomic nano-enzyme, extracting a macrophage membrane from natural macrophages, and coating the FeMnDA / BCNT nano material with the macrophage membrane to finally form M2 type macrophage membrane coated nano-particles, namely [MM] FeMnDA / BCNT. The [MM] FeMnDA / BCNT nano-enzyme prepared by the invention has good biological safety and simulated SOD and CAT enzyme activity, and the expression of inflammatory factors is reduced by removing excessive ROS (reactive oxygen species) in the cartilage cells induced by H2O2, so that the damage of oxidative stress to the cartilage cells is inhibited. The [MM] FeMnDA / BCNT nano-enzyme is not used for treating OA yet, so that a scientific basis is provided for further application and expansion of the diatomic nano-enzyme, and an effective strategy and a new thought are provided for treating osteoarthritis and chronic diseases related to oxidative stress in the future.