133 results about "Disease course" patented technology

The invention relates to the technical field of medical data processing and prediction, and discloses a chemotherapy adverse reaction prediction and intervention system based on big data. The system integrates an unstructured disease course text and structured inspection data of a patient, generates a time-series symptom event, and performs time window alignment and fusion on the time-series symptom event, a medication record and a physical sign monitoring stream to form a multi-dimensional time-series data block. The system is combined with an external medical knowledge base to construct a dynamic association network among symptoms, medicines and physiological indexes, and dynamically calculates the confidence coefficient of an adverse reaction mode according to real-time data. By using a predictive model of the timing attention mechanism, the system can output a continuous curve of patient risk over time. The system automatically matches and generates a personalized intervention instruction sequence containing specific measures and execution time windows according to key time points and modes when the risk curve exceeds a threshold value. According to the invention, dynamic and advanced early warning and accurate intervention of adverse reaction risks of chemotherapy are realized.

The invention relates to the technical field of medical image intelligent analysis and artificial intelligence auxiliary diagnosis, in particular to a medical image disease course prediction system based on an industrial neural network, and the system comprises a reference generation module which is used for obtaining static image data; processing the static image data by using a physical perception neural network to generate a pure ideal state reference; a perturbation simulation module; the industrial kinetic parameters are used as perturbation terms to be superposed to a pure ideal state reference, and a theoretical damaged state is generated; the projection verification module is used for acquiring real multi-modal observation data; generating a real residual error; generating a theoretical residual error based on the theoretical damaged state and the pure ideal state reference; calculating a manifold coupling confidence coefficient; the closed-loop correction module is used for performing inversion optimization on the industrial kinetic parameters; outputting a disease course prediction result according to the manifold coupling confidence coefficient; according to the method, the problem that a traditional medical model lacks physical consistency explanation is solved, and the credibility of artificial intelligence auxiliary diagnosis is remarkably improved.

The invention discloses an electronic medical record data synthesis method based on semantic processing, and relates to the technical field of medical informatization, and the method comprises the following steps: S1, constructing a probabilistic medical knowledge graph; s2, generating a semantic representation vector; s3, constructing a multi-dimensional dynamic space-time atlas; s4, generating a discrete personalized disease course event sequence with space-time coordinates; s5, taking the discrete personalized disease course event sequence and the corresponding medical entity semantic representation vector as condition input, guiding the improved TSDiff model to execute an iterative denoising process, and outputting a multi-dimensional random disease course trajectory; s6, forming multi-modal electronic medical record data; and S7, performing multi-dimensional quality evaluation on the multi-modal electronic medical record data. According to the method, the limitations of logic inconsistency, modal splitting and model capability solidification in a traditional synthesis method are overcome, and an efficient and accurate solution is provided.

The invention relates to the technical field of disease course trend modeling, in particular to a subarachnoid hemorrhage disease course trend modeling system fusing multi-source data. Four kinds of signals of intracranial pressure, blood flow velocity, cerebrospinal fluid pressure and electroencephalogram of a monitored object are synchronously collected, an instantaneous phase is extracted through Hilbert transform, a time window is adaptively adjusted according to the brain blood vessel conduction delay characteristic of an individual, and phase locking indexes among three pairs of signals are calculated. A multivariate coupled oscillator model is established, phase track topology invariant features are extracted, and comprehensive trend indexes are generated through tensor fusion. An individualized four-dimensional phase entropy baseline mode is established, and a double-layer early warning mechanism is adopted: when second derivative continuous symbol overturning occurs in all three phase locking indexes, early warning is directly performed, and when any two phase locking indexes are overturned, a trend index needs to be synthesized for confirmation. And predicting a state level, a trend level and an expected evolution trajectory based on a support vector regression model. According to the invention, precise disease course prediction and early warning are realized, and a basis is provided for clinical decision making.

The invention relates to the technical field of medical monitoring, in particular to an intelligent monitoring and health management system for liver, gall and pancreas postoperative drainage fluid, which comprises a data acquisition module for acquiring core specific indexes of liver, gall and pancreas special drainage fluid, visual images of the drainage fluid, dynamic flow and physiological data of a patient; the intelligent analysis module constructs a hepatobiliary pancreatic postoperative exclusive multi-modal model, deeply couples postoperative disease course time sequence characteristics to identify risks at different stages after the operation, and triggers risk assessment when the operation is abnormal; the early warning module is provided with a light, medium and heavy three-level mechanism, and carries out resource adaptation type grading accurate pushing to a responsible physician and a nurse station terminal in combination with a medical care real-time load and a spatial distance. The health management module fuses individual features of the patient and real-time monitoring data, and constructs a personalized rehabilitation scheme in combination with rehabilitation feedback of the patient; and the data synchronization module transmits model update parameters through an encryption channel, and perfects the complete-cycle health archive of the patient. Therefore, the problems that in the prior art, an early warning mechanism is rigid, and data security and full-period management are insufficient are solved.

The invention relates to the technical field of data analysis, can be applied to business system platforms of financial science and technology, medical treatment and health and the like, and discloses a human body state characteristic value analysis method, device and equipment and a medium. Performing system state analysis on an endocrine system, a circulatory system and a respiratory system of the user according to the physical examination data to obtain a system state characteristic value set, constructing a physiological index vector and a disease course characteristic vector according to the physical examination data, and constructing a complication incidence matrix according to case data; performing complication association analysis on the user according to the physiological index vector, the disease course feature vector and the complication association matrix to obtain a complication feature value, analyzing a criticality feature value of the user according to a system state feature value set and the complication feature value to obtain a hazard feature value set, and performing weighted summation on the hazard feature value set to obtain a complication feature value set; and obtaining a human body state characteristic value. And the accuracy of human body state characteristic value analysis is improved.

The invention provides a medical record generation optimization method based on type differentiation, and belongs to the field of natural language processing, and the method comprises the steps: constructing a medical record type classification system, designing an exclusive generation model and a scene rule for different types of medical records such as a first disease course, a daily disease course and a stage knot, and combining with a clinical feedback dynamic optimization generation strategy, thereby achieving the purpose of optimizing the medical record generation. The medical record generation method is suitable for scenes such as clinical medical record writing, medical teaching medical record construction and medical AI auxiliary diagnosis and treatment systems of hospitals at all levels.

The invention provides a big data resource service-based patient hierarchical intervention method and system, which are applied to the technical field of medical information, and are used for acquiring full-cycle health data of a patient, generating and updating a patient disease course trajectory sequence, analyzing health index change and trend under short, medium and long time scales, and calculating health scores and steady-state coefficients, so as to realize hierarchical intervention of the patient. A layered intervention scheme is generated, detection and personalized intervention of the health state of the patient are achieved, the timeliness and pertinence of medical intervention can be improved, and the disease management effect is optimized.

The invention discloses a complex medical quality management and control index automatic calculation method based on an intelligent agent. According to the index semantic model construction method provided by the invention, the automatic conversion of the medical quality control indexes from a natural language to structured semantics is realized, so that the index definition has computability and mobility, the dependence of manual analysis and script configuration is eliminated, and the standardization, generalization and reuse efficiency of the index definition is remarkably improved. Through multi-source data semantic packaging and an MCP service abstraction mechanism, semantic unification and interface standardization of multi-source heterogeneous data such as electronic medical records, inspection information, disease course records and medical advice management are achieved, and a semantic data layer capable of achieving cross-system access is constructed; the problems of data dispersion, field isomerism and interface incompatibility in a traditional system are effectively solved.

The invention provides a tumor patient group psychotherapy method and system based on artificial intelligence, and relates to the technical field of artificial intelligence. The method comprises the following steps: firstly, collecting demographic and disease course information, psychological scale scores, physiological signals and voice-text-face multi-modal data of a patient; emotional features are jointly recognized through a deep model, psychological needs are evaluated, clustering is carried out in combination with disease course stages, and patients are automatically distributed to homogeneous treatment groups. In the implementation process, the speaking balance degree, the topic dominant rate and the intra-group cohesion are calculated in real time, and if indexes cross the boundary, a guide instruction is generated to adjust discussion. And the system adaptively selects and dynamically adjusts a cognitive behavior therapy according to the emotion and interaction state, accepts a commitment therapy or a positive pressure reduction script until the emotion returns to a safety interval, carries out closed-loop summarization on intervention effect data, and outputs a report containing an emotion trend, interaction quality and an intervention effect. According to the method, real-time accurate evaluation, immediate intervention adjustment and continuous optimization can be realized.

The invention discloses a traditional Chinese medicine teaching and clinical simulation method and system based on a six-channel transmission theory, and particularly relates to the technical field of teaching simulation. The method comprises the following steps: acquiring disease course record data and symptom time sequence data, reconstructing a symptom evolution chain of each case and a corresponding six-channel identification label, and extracting six-channel disease course node time sequence characteristic data; the method comprises the following steps: constructing a single-path six-path transmission candidate network and a six-path transmission candidate topological network to obtain standard single-path six-path transmission network data and multi-branch six-path transmission topological structure data, and performing graph fusion and path weight re-calibration to generate six-path transmission topological network model data; and judging whether the current disease course node meets a trans-meridian bifurcation simulation triggering condition or not in combination with the symptom combination of the patient, and generating corresponding teaching feedback data and contrast learning data, so that a six-meridian transmission multi-branch nonlinear evolution process is presented in informatization teaching and clinical simulation scenes.

The invention provides a chronic respiratory system disease risk early warning method and device based on artificial intelligence and a medium. The method comprises the following steps: firstly, acquiring physiological monitoring information of wearable equipment of a patient; inputting the physiological monitoring information into the risk prediction model to obtain a risk early warning result; wherein the risk prediction model is established according to patient medical record information, follow-up visit information and historical physiological monitoring information; the risk prediction model is a Transform-LSTM (Long Short Term Memory) double-branch integrated model. According to the method, physiological monitoring information collected by wearable equipment in real time is input into a Transform-LSTM double-branch integrated model constructed on the basis of patient medical record, follow-up visit and historical physiological monitoring multi-source information, so that a long-distance dependency relationship of multi-modal data is captured by means of a Transform branch to identify a potential risk trend in a stable period; and the time sequence dynamic characteristics of physiological monitoring information are captured through an LSTM branch to perceive short-term signal mutation in an acute exacerbation period, so that accurate early warning of the whole course risk of the chronic respiratory system disease is realized.

The invention relates to the technical field of medical information processing, and discloses an intelligent analysis system for intensive care data, comprising: S1, an intervention anchor time base construction module for constructing a unified time reference based on equipment time service information and treatment intervention records, dividing intervention windows by taking an intervention event as a center, and generating an intervention anchor identifier; and S2, a data quality and working condition identification module. Intervention anchoring time base construction, data quality and working condition recognition, disease course trajectory modeling and evidence chain generation are sequentially executed under a unified time reference, and key nodes in a disease course trajectory map are condensed into decision capsules with evidence chain identifiers through a decision capsule generation module. Windowed alignment, quality layering and intervention response modeling are carried out on multi-source asynchronous monitoring data, so that the effects of continuously outputting intelligent analysis conclusions which are controlled in number, clear in basis and capable of being directly executed by medical care under intensive care complex working conditions, improving risk identification stability and reducing ineffective early warning interference are achieved.

The invention relates to the technical field of medical image processing and artificial intelligence, in particular to a skin cancer risk assessment method and system combining a vision and language model, and the method comprises the steps: collecting lesion images, extracting color edge textures, numbering abnormal regions, reading medical records, extracting symptom and time information, recombining semantic contents, matching image-text regions, and fusing expression fragments. And extracting continuous features, generating a combined sequence, mapping image-text contacts, and outputting a skin cancer risk identification result. According to the method, color jump and edge closure are dynamically read through a sliding area, a lesion area is marked, the structure capture granularity is improved, medical records are rearranged, symptom and time information is extracted, expression coherence is enhanced, the structure jump and symptom fragment blocks are compared, image-text mapping is established, and evolution consistency is guaranteed through sequence arrangement; boundary and disease course docking is clear in risk affiliation, and identification precision is improved.

The invention relates to the technical field of intelligent medical treatment, in particular to an AI-based brain disease test data management intelligent question-answering system, which comprises a multi-modal data acquisition module, an intelligent data processing module, an AI core model module, an interaction service module and a treatment guidance module, data aggregation is realized through a hospital interface, an equipment protocol and the like, and cleaning, standardization and multi-modal fusion are performed on multi-source data; precise question answering is achieved through the intelligent question answering unit, brain disease classification and stage division are completed through the patient layering unit, and a personalized scheme is generated by means of the treatment scheme generation unit; the interaction mode can be dynamically adjusted according to the cognitive level of a patient, emotion is recognized, guidance is provided, a doctor is supported to correct a scheme, and an iterative model is learned through feedback. The problems that in the prior art, the disease type is single, the data dimension is limited, and interaction adaptation is insufficient are solved, multi-disease-type whole-course precise management of brain diseases is achieved, and scheme scientificity and doctor-patient interaction experience are improved.

The invention discloses a medical record data association mining method and system based on reinforcement learning, and relates to the technical field of medical record data association mining, and the method comprises the following steps: obtaining medical record historical data, carrying out multi-modal feature extraction, and constructing a medical record data multi-modal state vector; constructing state input of a reinforcement learning agent based on the multi-modal state vector, constructing an action space and a reward evaluation mechanism, performing updating and iteration according to the reward evaluation mechanism, and outputting potential correlation information of the medical record; a world model for simulating a patient trajectory is introduced in the reinforcement learning process, patient state transition characteristics are learned according to medical record historical data, and a simulation sample for auxiliary training is generated for reinforcement training; performing interpretable output on the finally obtained medical record potential association information according to an enhanced training result, and constructing a medical record association network; the method effectively solves the problems that multi-modal heterogeneous data fusion is difficult, dynamic evolution of the disease course is difficult to model, and potential association interpretability is insufficient.

The invention relates to the technical field of image or video recognition or understanding, and discloses an areca yellows early warning method based on regular economic forest multi-dimensional symptom analysis, in the method, the unique morphology of areca and various morphological changes which are invisible under trees but visible in remote sensing during the period that areca suffers from yellows are fully utilized, and the areca yellows early warning effect is achieved. And the multiple symptoms are combined to form a multi-symptom matching degree, so that the misrecognition problem can be eliminated in an auxiliary manner when specific symptoms exist. When the specific symptoms do not exist, whether the disease of the current areca-nut forest belongs to the infectious disease or not is indirectly judged according to the distribution characteristics of the multi-symptom matching degree in time and space by utilizing the distribution characteristics and the disease course development rule when the areca-nut is cultivated as an economic arbor, and if yes, the disease of the current areca-nut forest belongs to the infectious disease; and considering that the areca yellows occupy a large proportion in the areca infectious diseases, the pathogen detection cannot be infeasible due to entrainment of a large number of irrelevant diseased plant samples when the pathogen detection is carried out at the moment. The above points are combined to realize early warning of areca yellows.

The purpose of the embodiments of this disclosure is to provide a control method and system for an adaptive electrical pulse generator. The method involves: collecting inflammation-related data from a user; extracting the user's biometric vector using a feature extraction module based on the inflammation-related data; determining the user's disease course classification using a disease course classification module based on the biometric vector; and then determining corresponding electrical pulse parameters using a parameter generation module based on the biometric vector and the disease course classification. According to the electrical pulse parameters, the adaptive electrical pulse generator applies a corresponding broadband electrical pulse stimulation scheme to the user at the electrical stimulation application site. This disclosure, through data fusion and closed-loop optimization, achieves precise personalization and dynamic adaptability in parameter generation, promoting the leap from static templates to intelligent dynamic modes in electrical pulse parameter configuration schemes.

The invention belongs to the technical field of diabetic foot clinical path management, and discloses a diabetic foot patient path management method and system based on lower limb vasculopathy grading. Mapping the ulcer position to the corresponding perfusion area and the artery segment to generate ulcer space characteristics; calculating a blood supply risk score of the perfusion area and generating a space risk map by combining the blood vessel image detection data and the microcirculation detection data; then collecting time sequence data including an ulcer index, an infection index, a blood supply index and a space risk map, and predicting the progress speed and the risk level of the future ulcer course; and finally, according to the blood supply risk score and the disease course prediction result, generating an individualized path management scheme through a joint triggering rule. According to the invention, precise and dynamic management of the diabetic foot ulcer is realized, the healing rate is improved, and the recurrence rate and the medical cost are reduced.

The invention provides a patient whole course monitoring method based on personalized guidance. The patient whole course monitoring method comprises the following steps that S1, physiological index data, behavior data, environment exposure data and medical record data of a patient are collected in real time to serve as multi-dimensional data; s2, judging the current disease course stage of the patient based on the multi-dimensional data through a preset stage recognition model; s3, predicting the type of the next disease course stage of the patient and the end moment of the current disease course stage according to the dynamic characteristics and historical data of the current disease course stage; s4, determining a transition stage based on the current disease course stage and the next disease course stage, and generating a monitoring scheme for the transition stage; and S5, generating a personalized guidance scheme according to the monitoring data of the transition stage, so that the physiological fluctuation and nursing requirements of the patient during stage conversion can be dynamically matched, disjunction or excessive intervention can be avoided, the individual tolerance difference can be adapted, and the continuity and safety of whole course management can be improved.

The invention relates to the technical field of sepsis monitoring, and discloses a sepsis monitoring method and system based on multi-modal data, and the method comprises the steps: setting a plurality of sepsis patient types based on influence factors, and generating a plurality of disease course period sequences which comprise a plurality of disease course periods, each disease course period is mapped with a corresponding multi-modal data set; comparing and analyzing the multi-modal data sets of the plurality of disease course cycle sequences to obtain feature data of each disease course cycle, change features of each feature data and a weight coefficient, and constructing a sepsis monitoring model; the real-time characteristic data of the to-be-monitored patient is obtained and input into the sepsis monitoring model, the monitoring result is obtained, whether the early warning instruction is generated or not is judged, the complexity of sepsis and the individual difference of the patient are fully considered, the monitoring accuracy is improved, and powerful support is provided for timely diagnosis and treatment of the sepsis patient.

The invention discloses a herpes zoster skin lesion staging evaluation method and system based on AI image recognition, and relates to the technical field of herpes zoster skin lesion staging evaluation, and the method comprises the following steps: collecting herpes zoster skin lesion image data continuously recorded in multiple periods, synchronously embedding a time identifier in the collection process, and carrying out the time identification; recording the shooting moment, the illumination state and the shooting angle information of each frame of image in a unified manner to form an original image sequence; and based on the original image sequence, according to the progressive sequence of the time identifiers, performing adaptive sorting adjustment on the time intervals between the adjacent frames. According to the method, a stable skin lesion time evolution track is constructed through time identification and a continuous processing flow, so that staging reverse judgment caused by frame order disorder is avoided, and the continuity and reliability of herpes zoster skin lesion staging evaluation are improved; and meanwhile, color, area and texture changes are uniformly incorporated into natural time rhythm constraints, so that a staging result better conforms to a clinical disease course rule, and the safety of remote follow-up visit and intelligent evaluation is improved.

The invention relates to an intelligent medical record generation method and system. The method comprises the following steps: extracting each piece of data in accessed multi-type data and constructing a time-based event; constructing a directed graph arrangement event; traversing the directed graph, and verifying whether an event conforms to a rule base or not by using the rule base containing causal logical reasoning in the traversing process; and traversing the directed graph based on the medical record generation condition, and sorting the traversing result based on the generation type to obtain a medical record document. According to the method, multi-source data are fused in the generation process, and cross-modal and cross-cycle long text integrated generation is achieved; performing real-time calling and verification on related knowledge based on the ternary knowledge base to realize dynamic enhancement; a time-event chain reasoning mechanism and fine-grained reference are realized based on a directed graph, so that the traceability of the time sequence, causal logic and information sources of disease course records is ensured, posterior verification and feedback after quality inspection are supported, and a high-quality medical record generation basis is provided.

The invention relates to a tailorable anti-fungal multi-point penetrating press-fit sewing dressing and preparation and application thereof.The method comprises the following steps that a high polymer material solution and an anti-fungal medicine solution are mixed, liquid-phase matter is subjected to defoaming treatment, spinning is conducted, curing is conducted through a fixing solution, and the tailorable anti-fungal multi-point penetrating press-fit sewing dressing is obtained; performing rinsing, drying, opening, needling, freeze-drying and vacuum drying on the obtained condensate, and then compounding the condensate with a gelatin layer and a surface layer to obtain the tailorable antifungal multi-point penetrating press-fit sewing dressing. Compared with the prior art, the preparation method has the advantages that an antifungal drug is introduced and compounded with a high polymer material, the compound is prepared into the drug-containing fiber, and then the drug-containing fiber layer and other material layers are prepared into a final product through a modern textile technology. The product can realize long-acting sterilization and bacteriostasis, and solves the problems of difficult administration, poor curative effect, repeated disease course, patient compliance and the like of body surface fungal infection at present.

The application discloses a kind of probe set and its combination 3D printing microfluidic chip to the method for analyzing specific glycoprotein sialic acid on exosome surface, belong to biochemical analysis technical field.The present application is based on 3D printing microfluidic chip combined with three-probe co-localization signal amplification strategy, nucleic acid signal is converted into fluorescent signal, by directly reading fluorescent signal intensity, realize the analysis of sialic acid signal on the glycoprotein on the surface of exosome, which provides a new method for overcoming the defects and limitations of exosome clinical application in the prior art, reduces the loss of sample, improves the sensitivity of reaction, and provides a feasible method for exploring the possible relationship between different exosomes and their parent cell specific glycoprotein sialic acid and disease development, and using sialic acid for tumor screening and disease course determination.

The presently disclosed subject matter discloses a system and method for predicting at least one outcome in a subject suffering from an inflammatory bowel disease, the system comprising a processing circuitry configured to: obtain (i) a series of frames of the subject's gastrointestinal tract, and (ii) a machine learning model capable of receiving a series of frames of the gastrointestinal tract of a given subject suffering from inflammatory bowel disease and predicting one or more outcomes associated with the disease course in the given subject; and, predict, utilizing the obtained series of frames of the subject's gastrointestinal tract and the machine learning model, at least one outcome in the subject suffering from the inflammatory bowel disease.

The invention discloses a method for symptom information missing detection and a related product, and the method for symptom information missing detection comprises the steps: obtaining a to-be-detected medical record document which comprises at least two disease course records; extracting a semantic vector of a first disease course record fused with semantic information of other disease course records; extracting a potential symptom vector corresponding to symptomatic drug information in the course of disease record; fusing the semantic vector of the first disease course record and the potential symptom vector of the disease course record to obtain a feature vector of the disease course record; and determining the missing symptom information of the medical record document according to the feature vectors of all the disease course records in the medical record document. According to the method, the missing symptom information in the medical record document can be accurately detected.

The invention provides an epileptic information management method and system, and relates to the technical field of medical information management. The method comprises the following steps: firstly, performing standardization processing on multi-source heterogeneous data of a patient, and constructing a multi-dimensional patient portrait containing static, dynamic and time sequence features; secondly, performing multi-level clustering on historical patients based on treatment response and disease course evolution similarity; generating a management suggestion according to the association degree of the to-be-managed patient and the clustering cluster and the current management scene; and finally, dynamically updating the portrait and the clustering affiliation based on treatment feedback. According to the invention, by mining time sequence features and establishing a closed loop feedback mechanism, precise classification and dynamic personalized management of epileptics are realized, and the accuracy of clinical treatment recommendation is significantly improved.

This invention relates to the fields of medical data analysis and artificial intelligence, proposing a dual-latent variable decoupled data analysis method based on plasma Netrin-1 and clinical scale features. First, plasma Netrin-1 test data and clinical characteristics such as UPDRS scale scores and disease duration are acquired from the subjects. Batch effect correction is applied to the raw test values, and an input feature vector is constructed. Then, a variational autoencoder model is established. By setting orthogonal constraints, Netrin-1 regression constraints, and disease duration regression constraints, the latent space is structurally constrained, enabling the model to learn latent variables D representing disease progression and C representing adaptive changes during training. Based on this, an efficiency index η composed of latent variables C and D is calculated, and relevant trend indicator parameters are obtained by combining longitudinal follow-up data. This method can achieve decoupled representation of disease-related features and adaptive change features in multi-source clinical data, providing a new data analysis tool for statistical analysis and research of neurodegenerative disease-related data.

The application discloses a prediction method and device for the influence of different stage PM2.5 components on diseases and a medium; the method comprises the following steps: collecting sample data of a prospective cohort and preprocessing the sample data to obtain a standardized feature dataset; constructing a multi-state trajectory model; using the multi-state trajectory model to calculate the risk ratio and dynamic transition probability of transition between diseases under different PM2.5 pollutant exposures; constructing an accelerated failure time model; using the accelerated failure time model to quantify the time ratio of transition of each disease state under PM2.5 pollutant exposure, identify the acceleration effect of the disease course, and output the predicted time of state transition; and outputting the risk ratio, dynamic transition probability and predicted time of state transition as the prediction result. The application can improve the accuracy of predicting the dynamic and acceleration effects of PM2.5 components on the whole disease course of metabolic cardiovascular diseases, and provide a scientific decision basis for early health intervention of metabolic cardiovascular diseases.