300 results about "Patient characteristics" patented technology

The invention provides a cerebral hemorrhage personalized treatment scheme optimization method and system based on big data analysis, and relates to the technical field of data analysis, and the method comprises the steps: constructing a cerebral hemorrhage patient multi-source heterogeneous clinical database, extracting feature subsets, carrying out semantic alignment, carrying out time sequence modeling and fusion, and obtaining a patient panoramic feature representation; constructing a cerebral hemorrhage diagnosis and treatment knowledge graph, adding the cerebral hemorrhage diagnosis and treatment knowledge graph to a preset graph attention network, distributing weights, obtaining knowledge-enhanced patient feature representation and taking the knowledge-enhanced patient feature representation as a state, generating an action space based on an optional treatment scheme, constructing a reward function and determining a candidate treatment scheme; performing medication suitability analysis, performing semantic conflict identification in combination with the patient allergy history and complication, and performing screening to obtain a suitable candidate treatment scheme set; and for each scheme, predicting a multi-dimensional prognosis index, constructing a multi-criterion decision matrix, combining personal influence factors, adjusting the weight of each index, modeling an optimization problem and determining an optimal treatment scheme.

The invention provides a scientific and technological literature intelligent retrieval method and related equipment based on generative artificial intelligence, and the method comprises the steps: carrying out the multi-layer semantic annotation of medical scientific and technological literatures, constructing a symptom-disease dynamic association map, and building a medical scientific and technological literature knowledge base; performing medical context analysis and multi-modal feature extraction on user query, and generating a unified retrieval vector in combination with Boolean operation nested analysis and semantic alignment processing; performing evidence grading retrieval and clinical scene matching based on the unified retrieval vector to obtain a preliminary candidate literature set, and optimizing the preliminary candidate literature set into a target candidate literature set through fine-grained semantic recalculation; and calculating a retrieval prior probability according to the evaluation dimension, performing knowledge weighted fusion on the candidate literature, and generating a medical science and technology literature recommendation report. According to the method, the medical term association relationship is deeply understood through the association map, the result is ensured to be matched with the patient characteristics through evidence grading retrieval and clinical scene matching and screening, and the accuracy of document retrieval is improved.

The invention relates to the technical field of auxiliary treatment of traumatic patients, in particular to a construction method of an auxiliary treatment system based on traumatic patients. The method comprises the following steps: carrying out multi-modal physiological data acquisition and physiological data preprocessing on a trauma patient through hospital examination equipment to obtain multi-modal feature fusion data; carrying out physiological feature vector generation on the multi-modal feature fusion data to obtain a physiological feature vector of the trauma patient; carrying out physiological state coding on the physiological feature vector of the trauma patient to obtain physiological state coding data; acquiring trauma medical knowledge data; and according to the trauma medicine knowledge data and the physiological state coding data, carrying out physiological state and knowledge graph association to obtain physiological state and knowledge graph association data. According to the invention, through real-time adjustment of the patient processing sequence and automatic generation of the personalized resource allocation auxiliary processing scheme according to the patient features, the medical resource utilization efficiency is improved.

The invention discloses a personalized hypertensive nephropathy risk assessment method and system based on a graph convolutional network, and the method comprises the steps: collecting modal data related to hypertensive nephropathy, and carrying out the preprocessing; constructing a patient similarity graph; performing multi-source information fusion and domain knowledge integration; sparse processing is carried out on the graph structure, and standardization processing is carried out on an adjacent matrix; modeling through a time sequence diagram convolutional network and a long-short term memory network, and capturing the evolution trend of patient characteristics along with time; through a self-attention mechanism and a graph regularization item, the transmission of important features is enhanced; a multi-head attention mechanism is adopted, and different types of relation modes are captured; optimizing model parameters by using an Adam optimization algorithm and a regularization method; and a risk assessment result is optimized by adopting weighted stacking and a self-adaptive attention mechanism. According to the risk assessment method, accurate assessment of the risk of hypertensive nephropathy is realized by constructing the multi-dimensional and dynamically changing patient similarity graph.

The invention relates to the technical field of nursing scheme intelligent recommendation systems, in particular to a cardiology patient personalized nursing scheme intelligent recommendation system optimization method, which comprises a dynamic rare case recognition engine for accurately capturing and recognizing cardiology rare cases by using medical data and text data, and comprises a data preparation module, a data retrieval module, a data processing module and a data processing module, the candidate sample classification and recognition module is used for recognizing whether rare cases exist or not, and the problems existing in the prior art are effectively solved by introducing innovation modules such as a dynamic rare case recognition engine, a knowledge graph builder and a rare case simulation generator. According to the invention, continuous learning and updating can be realized, rare cases can be accurately identified, and customized nursing suggestions can be provided based on individual features of patients. Meanwhile, the decision-making process of the system has good interpretability, decision-making basis can be provided for doctors, and usability and credibility of the system in clinical practice are greatly improved.

The invention discloses a tumor patient medication side effect management method and system based on big data analysis, and the method comprises the following steps: collecting and preprocessing patient data and drug data, and extracting patient features and drug features, the patient data comprising genome data provided by a patient; constructing a prediction model which comprises a time sequence analysis sub-model, a gene-drug interaction analysis sub-model and a risk score generation sub-model and is used for predicting risk scores of different types of side effects of the patient by taking the drug characteristics and the patient characteristics as input of the prediction model; inputting the basic information, the medication information and the genome data of the current patient into the trained prediction model to obtain risk assessment of various side effects of the current patient; according to the risk assessment result, personalized adjustment suggestions suitable for the patient are selected from a preset intervention scheme library and provided for the current patient. The scientificity and accuracy of administration side effect management in the treatment stage can be effectively improved.

The present disclosure provides systems and methods for providing neurostimulation therapy according to patient features. The patient features may be analyzed to develop a patient model between physiological and / or patient reported features and optimal settings for a neurostimulation therapy using machine learning operations. The model is trained using data corresponding to time intervals in which electrical pulses are applied to the patient, including video kinematic data, patient reported pain levels, implanted or worn sensor patient data, and stimulation parameters. The model is used to control ongoing neurostimulation therapy for the patient.

The invention discloses a cervical LSIL progress risk prediction method and system based on multi-modal time sequence fusion, and the method comprises the steps: collecting multi-modal data, and carrying out the standardization processing; extracting dynamic change characteristics in continuous annual TCT liquid-based pictures through a convolutional neural network, and positioning a high-risk cell region; carrying out interval sensing position coding on HPV detection records, constructing an inter-modal causal attention mechanism, and establishing a time sequence causal relationship between HPV infection events and cell abnormal evolution; a discrete time competition risk model is adopted, the progression, regression and maintenance probabilities of different time periods in the future after primary diagnosis are synchronously output, a time-varying covariable LSTM is introduced, and the weight of patient features changing along with time is dynamically updated; and generating a cell evolution thermodynamic diagram, marking a high-risk area space-time evolution path, outputting a time influence curve, and marking a key risk accumulation time window. According to the invention, accurate quantitative evaluation of the cervical low-level lesion progress risk is realized.

The invention provides a tumor patient clinical test matching system and method based on a large language model and an OCR technology, and is applied to the field of medical data processing. The method comprises the following steps: analyzing clinical data and test information, processing an unstructured text, and generating structured clinical feature data through context association analysis; key data is extracted and subjected to double verification correction, and structured data supplementary information is generated; enhancing the structured clinical feature data and supplementary information based on a multi-modal processing assembly line module, extracting an image quantitative index, analyzing an immunohistochemical result, and generating a comprehensive matching score; through a rule engine and semantic similarity calculation, item-by-item comparison of patient features and entry and exhaust conditions is realized, and a preliminary matching result is generated; edge case misjudgment is corrected through context-aware multi-round reasoning, sorting is adjusted in combination with clinical test priority weights, and an optimized clinical test matching list is generated; and generating a clinical test matching report based on the data.

The embodiment of the invention discloses a spinal metastatic tumor treatment scheme generation system. According to one specific embodiment, the system comprises a data processing server, an information fusion server and a scheme generation server which are in communication connection with one another, and the data processing server is used for preprocessing multi-source patient data to obtain standard multi-source patient data; the information fusion server is used for executing the following steps: performing feature code fusion on standard multi-source patient data to obtain a multi-source patient feature vector; performing feature enhancement on the multi-source patient feature vector to obtain a joint patient characterization vector; generating an initial therapeutic schedule result based on the joint patient characterization vector; and the scheme generation server is used for performing feature decision processing on the initial treatment scheme result to obtain a final treatment report. According to the embodiment, waste of computing resources can be reduced, and system response time can be shortened.

Methods and systems for treatment prioritization using a neural network model are provided. Training the model includes generating an enriched genetic knowledge graph with edges characterizing relationships between genetic variants, genes, diseases, treatments and symptoms, embedding the graph vertices in genetic knowledge vectors, receiving training patient feature value sets, generating a training dataset of training patient vectors labelled with target values comprising treatment efficacity by scaling the genetic knowledge vectors by the patient feature value sets, and training the model to process an input patient vector and generate a predicted efficacity of the at least one treatment option. Performing inferences includes receiving patient feature values characterizing a patient, generating the patient input vector by scaling the genetic knowledge vectors by the patient feature values, and providing the patient input vector as input to the model, thereby generating a treatment efficacy vector predicting efficacity of a plurality of treatment options.

The embodiment of the invention discloses a depression probability determination method and system and a related device.The method comprises the steps that data sets of N window numbers corresponding to target electroencephalogram signal data of a patient to be detected are obtained through a preset filter, the data sets are input into a trained prediction model, prediction tags are correspondingly obtained, and the prediction tags are used for prediction of the target electroencephalogram signal data of the patient to be detected; according to the N prediction labels, determining the probability of depression of the to-be-detected patient, and taking denoising generation data output by training a preset conditional diffusion model based on the electroencephalogram signal data and the number information of the multiple subjects and the tag information about whether the subjects suffer from depression or not and the initial electroencephalogram signal data of the multiple subjects as a training set of a prediction model. According to the method, the number information and the label information are used as features to be embedded into the diffusion process, so that individual features and depression features of the patient can be better introduced, the condition diffusion model is denoised to generate electroencephalogram signals with more individual differences, and the accuracy of the prediction probability value of the prediction model is improved.

The invention relates to the technical field of anesthesia, and discloses a personalized anesthesia scheme generation method, which comprises the following steps: S1, extracting and coding patient features; s2, modeling a pharmacokinetic model; s3, drug concentration prediction; s4, applying a PD model; s5, performing uncertainty quantification; s6, predicting and outputting; s7, updating and feeding back parameters; s8, verifying the model; in the personalized anesthesia scheme generation process, firstly, effective feature extraction and coding are carried out on multi-modal data of a patient; according to the personalized anesthesia scheme generation method, efficient feature extraction is carried out on multi-modal data (such as electronic health records, genome information and real-time monitored vital signs) through VAE and other deep learning methods, and the differences of patients in the aspects of physique, drug metabolism capability, gene background and the like can be fully described; therefore, the optimal anesthetic medication strategy is customized for each patient.

The invention discloses a tumor postoperative treatment staged planning method based on artificial intelligence. The method comprises the following steps: S1, constructing a multi-modal medical data set; s2, performing standardization processing on the acquired multi-modal medical data set to form a standardized data set which can be used for feature extraction; s3, generating a unified patient feature vector; s4, according to the characteristics of tumor postoperative treatment, dividing the whole treatment process into a postoperative immediate recovery stage, a postoperative recovery middle stage and a long-term follow-up visit stage, and defining a corresponding optimization target for each stage; s5, based on the patient feature vector and the optimization target of each stage, generating an initial treatment plan by using a deep ant colony algorithm; and S6, outputting a staged treatment scheme. According to the method, the professional and technical thresholds required by traditional modeling are reduced, and the universality and convenience of three-dimensional modeling are improved.

The present invention relates to a system, a method, a computer program and a computer program product for automatic planning of a clinical intervention for implanting a cardiac valve in a patient. The system (100) comprises an input interface (101) for entering data and a computing unit (102). Said computing unit (102) is adapted to receive input data relating to characteristics of the patient via the input interface 101), to automatically select a surgical tool (VT, DT) and a surgical method characteristic (AS, VP, PS) based on the characteristics of the patient, the surgical tool (VT, DT) and the surgical method characteristic (AS, VP, PS) being suitable for implanting the cardiac valve in the patient, and to provide output data relating at least to the selected surgical tool (VT, DT) and the selected surgical method characteristic (AS, VP, PS).

The invention requests to protect a dual-core decision-making examination reservation system based on a rule engine and deep reinforcement learning. The dual-core decision-making examination reservation system comprises a data infrastructure layer, an intelligent decision-making layer and a decision-making output layer, wherein the data infrastructure layer is used for forming reservation data according to data of equipment and patients; the intelligent decision-making layer is used for constraining patient appointment according to the appointment data; and the decision output layer provides a scheduling scheme according to the reservation data and the constraint conditions. The dynamic state space constructs a higher-dimensional state vector space, the reservation data comprises a patient feature domain, an equipment state domain and an environment dynamic domain, and comprehensive dynamic data support is provided for decision making in combination with personalized features of the patient and real-time environment information. The intelligent decision-making layer has a hybrid decision-making mechanism and comprises a rule decision-making module and an optimization module, the rule decision-making module verifies hard rules of the reservation data, the optimization module performs weighted integration on non-hard rules of the reservation data, and finally the decision-making layer outputs a scheduling scheme.

According to the time sequence risk control method and system based on the multi-level mixed experts, heterogeneous data in the medical field is fused and mapped to the unified representation space through multi-modal representation learning, and therefore data information is utilized more comprehensively. The system adopts a multi-level hybrid expert (MoE) architecture, tasks can be accurately routed to a more professional expert group according to patient characteristics through a hierarchical gating network, and the accuracy and resource utilization efficiency of the model are greatly improved. Meanwhile, through bidirectional interpretability and a causal inference mechanism, not only can the reason of a risk prediction result be explained, but also anti-fact analysis and specific intervention measure guidance can be provided, so that the model is converted from a pure prediction tool to an intelligent partner for auxiliary decision making.

The invention relates to the technical field of medical image diagnosis, and discloses a tooth and fracture line recognition treatment method based on the combination of an AI technology and a CBCT image, and the method comprises the steps: obtaining and preprocessing the CBCT image, and carrying out the multi-scale analysis and recognition of a tooth structure, a microcrack and a fracture line through a first AI model. And the second AI model combines the identification result and the patient characteristics, and generates a personalized treatment scheme through multi-objective optimization. Clinical feedback is used for continuously iteratively optimizing double models, and the diagnosis and treatment precision and effect are improved. The system comprises an image data acquisition unit, an image data preprocessing unit, a tooth and fracture line identification unit, a personalized treatment scheme generation unit and a feedback and optimization unit. Through AI and CBCT image fusion, accurate identification of teeth and fracture lines is realized, a personalized treatment scheme is recommended in combination with individual features of a patient and a multi-objective optimization algorithm, rapid response is realized, a closed-loop feedback mechanism continuous optimization model is established, and diagnosis and treatment precision, efficiency and individualization level are remarkably improved.

The invention discloses a glioma radiotherapy postoperative risk assessment method based on a magnetic resonance image, particularly relates to the field of glioma radiotherapy patient health risk assessment, and is used for solving the problem that an existing assessment mode depends on manual interpretation and is difficult to predict bad clinical outcomes in advance. The method comprises the following steps: performing clinical data gridding reconstruction on a corticoid use cycle of a patient and tumor molecular typing, and combining morphological characteristics of an edema region in a magnetic resonance image to generate time-aligned clinical comprehensive characteristic vectors; mining a frequent association item set between the comprehensive feature vector and the pathological process to construct a mapping relation model; establishing a probability graph reasoning model of the bad outcome based on the pathological process vector sequence and the probability weight; and integrating the two types of models to form a causal reasoning network, and inputting a target patient feature vector to calculate an accumulated risk value of reaching a bad outcome. According to the method, an interpretable individual risk assessment result can be output, and a basis is provided for postoperative follow-up visit and intervention.

The invention discloses a semi-supervised prognosis prediction system based on irregular sampling medical data pre-training, and the system comprises a clinical electronic medical record data collection and preprocessing module which automatically collects original clinical electronic medical record EHR data from a medical database; and the pre-training module is used for receiving the patient feature vector sequence output by the clinical electronic medical record data acquisition and preprocessing module and carrying out feature representation learning on the preprocessed clinical time sequence data by utilizing a combined multi-task self-supervised learning mechanism. And the classifier fine tuning and pseudo-label iterative optimization module is used for training the pre-trained model through fine tuning of samples with labels and carrying out iterative optimization through guidance of pseudo-labels to obtain a prediction result. And the application display module is used for displaying and outputting a post-hospital-admission vital sign sequence and a prediction result. According to the method, irregular sampling and missing data are effectively processed, information loss is avoided, and the prediction capability of the model and the performance of the model under the conditions of data imbalance and label scarcity are improved.

The invention discloses a rapid progression type nasopharyngeal carcinoma risk prediction method based on an artificial neural network, and relates to the field of medical informatics crossing. The invention provides a rapid progressive nasopharyngeal carcinoma risk prediction method based on an artificial neural network, and aims to solve the problem that a rapid progressive nasopharyngeal carcinoma patient is difficult to recognize in time by depending on TNM staging and experience judgment in the prior art. According to the method, historical case data collection, missing value filling and standardization preprocessing, core feature determination through feature screening, class imbalance correction, feature coding and feature matrix construction are sequentially carried out, an artificial neural network model is trained and optimized under a cross validation framework, and performance and threshold values are determined on a validation set. During clinical application, patient features are input, and the model outputs a rapid progress risk probability and a risk level. Compared with a conventional staging or linear model, the method can improve the prediction accuracy, and achieves the early recognition and individualized treatment of a high-risk patient.

The present invention discloses a method and system for managing the side effects of medication for tumor patients based on big data analysis, the method comprising the following steps: collecting and preprocessing patient data and drug data, extracting patient characteristics and drug characteristics, wherein the patient data includes genomic data provided by the patient; constructing a prediction model, including a time series analysis sub-model, a gene-drug interaction analysis sub-model and a risk score generation sub-model, for using drug characteristics and patient characteristics as inputs of the prediction model to predict the risk score of different types of side effects in the patient; inputting the basic information, medication information and genomic data of the current patient into the trained prediction model to obtain a risk assessment of various side effects in the current patient; according to the risk assessment results, selecting a personalized adjustment suggestion suitable for the patient from a preset intervention program library and providing it to the current patient. The present invention can effectively improve the scientificity and accuracy of the management of medication side effects during the treatment stage.

The invention provides a drug recommendation system and method based on causal inference, and relates to the technical field of data mining, the system uses a deep learning model, and specifically comprises a patient state encoder, a medical examination auto-encoder and a recommendation module; the method comprises the following steps: inputting a patient feature X, and projecting the X to a low-dimensional embedding space by using a neural network encoder; reconstructing the medical examination data R by using a coder-decoder structure to obtain a substitute low-dimensional hidden variable ZR of the R, and calculating reconstruction loss; using comparative learning to optimize the low-dimensional hidden variable ZR, and calculating comparative learning loss; according to the patient characteristic X and the hidden variable ZR of the medical examination, predicting an appropriate drug, and calculating recommended loss; and the reconstruction loss, the comparison learning loss and the recommendation loss are jointly optimized to optimize parameters of the drug recommendation system, a causal relationship P (Ydo (X)) between the patient and the drug is established, and the recommendation ability of the drug recommendation system is improved. And finally, performing drug recommendation by using a drug recommendation system through combining the patient characteristics and the medical examination data.

The invention provides a Bayesian network and rough set feature selection method for pneumonia pathogenesis diagnosis, belongs to the technical field of medical information intelligent processing, and solves the technical problems of symptom overlapping, tag ambiguity and data feature redundancy in pneumonia pathogenesis diagnosis. According to the technical scheme, the method comprises the following steps of S1, performing data preprocessing on pneumonia data of a patient; s2, taking the patient features and the disease cause labels as nodes, constructing a Bayesian network to solve the disambiguation problem of the disease cause labels, and generating a label confidence coefficient matrix; s3, based on the neighborhood rough set, calculating the dependency degree of each feature on the disease cause label; and S4, screening an optimal feature set in combination with significance analysis and redundancy reduction. The method has the advantages that the pneumonia diagnosis scene serves as the application background, the problems of pathogenesis complexity and data redundancy of the patient can be effectively solved, the diagnosis reliability and efficiency are improved, the calculation cost is reduced, and technical support is provided for actual clinical diagnosis.

The invention discloses a Chinese patent medicine prescription intelligent assistance method and system, and the method comprises the steps: determining a possible traditional Chinese medicine diagnosis range through disease information / patient information, carrying out the further analysis, obtaining the disease feature information / patient feature information which needs to be obtained for determining the traditional Chinese medicine diagnosis, and determining the traditional Chinese medicine diagnosis according to the related information. The Chinese patent medicine prescription intelligent auxiliary system comprises a demand acquisition module, a feature information analysis module, an information acquisition module and a traditional Chinese medicine diagnosis analysis module. According to the Chinese patent medicine prescription intelligent assistance method and system, doctors and patients can be helped to accurately select Chinese patent medicines, and damage caused by misuse of the medicines is prevented.

The invention discloses a sepsis clotting disease subtype characteristic and typing system, which comprises a basic data acquisition module used for acquiring SIC patient characteristics; a potential category analysis module configured to identify potential groups of the SIC patients based on the SIC patient features; the K-means clustering module is configured to cluster the characteristics of the SIC patients and obtain a clustering result of the SIC patients; and the typing result output module is configured to output subtype features and typing results of the SIC patients based on the potential groups of the SIC patients and the clustering results of the SIC patients. The subtype classification of the SIC patient can be rapidly and accurately analyzed, and a direction is provided for subsequent treatment.

The invention discloses a traditional Chinese medicine prescription intelligent recommendation method based on a multi-source heterogeneous graph, a medium and equipment, and the method comprises the steps: firstly extracting a patient symptom feature vector through a natural language processing technology, and constructing a patient comprehensive feature in combination with a physical feature vector; generating a prescription feature vector based on prescription composition, efficacy classification and historical diagnosis and treatment data; a heterogeneous graph knowledge graph containing nodes of symptoms, physiques and prescriptions is constructed, node features are iteratively aggregated by using a graph convolutional network, and deep correlation modeling among symptoms, physiques and prescriptions is realized. And finally, a personalized recommendation result is output by calculating a matching score of the patient characteristics and the prescription nodes. According to the method, modern clinical data and the theory of traditional Chinese medicine are creatively combined, the accuracy, individuation and clinical applicability of prescription recommendation are remarkably improved through dynamic map construction and deep learning technologies, and an effective solution is provided for intelligent diagnosis and treatment of traditional Chinese medicine.

The invention discloses a tumor patient portrait construction method and system based on multi-source heterogeneous data. The method comprises the following steps: collecting heterogeneous data related to a tumor patient; preprocessing the heterogeneous data, including format standardization, missing value completion, time sequence alignment and privacy desensitization processing, to obtain a data set which can be used for unified modeling; respectively extracting disease features, psychological features, behavior features, social features and regional features, and generating corresponding feature vectors for various features; mapping the feature vectors to a unified feature space by using a heterogeneous graph neural network based on an attention mechanism, dynamically calculating contribution degrees of different features to patient portraits, and establishing a multi-dimensional feature association graph of the tumor patients; and generating a structured tumor patient portrait including a disease dimension, a psychological dimension, a behavior dimension, a social dimension and a regional dimension. According to the method, high-precision, multi-dimensional and explainable patient feature description is realized, and a data basis is provided for precise service.

A computer-implemented method of determining one or more sets of features to predict the presence of a particular phenotypic characteristic comprises: (a) receiving patient data comprising, for each of a plurality of patients: a feature profile comprising a respective feature status for each of a plurality of features for that patient; and an indication of whether that patient expresses the particular phenotypic characteristic; (b) using a genetic algorithm to generate a plurality of generations of individuals, wherein each individual comprises a subset of the predetermined plurality of features, each generation of individuals generated based, at least in part, on a plurality of fitness scores, each fitness score corresponding to a respective individual in the previous generation, and parameterizing a predictive accuracy of the set of features, each fitness score being calculated based at least in part on the patient data; (c) repeating step (b) until it has been performed N times; (d) from the plurality of individuals generated in steps (b) and (c), selecting a subset of the individuals based on their fitness scores; (e) clustering the selected subset of individuals to generate a plurality of clusters of individuals, based on the similarity of their respective subsets of features; (f) from each cluster, identifying a respective characteristic feature set based on the frequency with which features appear in individuals in that cluster.

The invention provides a cardiovascular and cerebrovascular disease classification model construction method, which comprises the following steps: receiving dynamic signal data of a cardiovascular and cerebrovascular disease patient, and constructing a dynamic signal matrix; global pathological features of patients with cardiovascular and cerebrovascular diseases are collected to serve as static feature vectors, and time dimensions of the static feature vectors and the dynamic signal feature matrix are unified to construct a fusion feature matrix; constructing a common disease association network; when patient group grouping is carried out, similarity mapping from individuals to groups is carried out through similarity calculation of features of each time slice and a group feature center to generate patient group features, and the patient group features are aligned with a patient feature center matrix; and constructing a deep classifier for cardiovascular and cerebrovascular disease classification, and finally outputting a classification result by the classifier. The method has significant breakthroughs in the aspects of dynamic feature modeling, disease relevance modeling and personalized adaptation capability, and an efficient and accurate technical means is provided for cardiovascular and cerebrovascular disease classification.