591 results about "Medical knowledge" patented technology

An intelligent analysis method based on medical document structure perception and multi-modal fusion comprises the following steps: carrying out structure topology modeling on a medical document, extracting visual layout, text meta-information, space coordinates and semantic keyword features, constructing a semantic topological graph and dynamically shielding irrelevant contents; selecting an extraction path according to a document type, performing deep semantic analysis and entity recognition on a text-type document, and performing visual enhancement OCR recognition on a scanning-type document; the features are injected into a medical knowledge graph, and feature fusion, semantic verification, relation reasoning and information completion are achieved through a graph neural network; a three-stage strategy optimization model of basic pre-training, domain adaptation and online reinforcement learning is adopted; and large-scale processing is realized through a dynamically aggregated distributed architecture. The method is used for intelligent analysis and structured conversion of documents of hospitals, medical insurance and medical scientific research. The problems that heterogeneous medical document analysis adaptability is poor, multi-modal fusion is difficult, medical knowledge utilization is insufficient, and large-scale processing efficiency is low are solved.

The invention discloses a medical clinical decision support method and system based on a knowledge graph, and the method comprises the following steps: S1, collecting structured and unstructured medical data, and constructing an initial medical knowledge graph; s2, performing term standardization and semantic alignment on the graph to generate a fusion knowledge graph; s3, constructing a time-labeled medical record graph structure based on the medical record data, and aligning the time-labeled medical record graph structure with the fusion graph; s4, inputting the fusion atlas and the medical record graph into the hypersphere graph neural model, and generating semantic representation; s5, calculating a gravitation vector by using a path traction module, and guiding the propagation direction of the reasoning path; s6, generating a diagnosis and treatment candidate set and a corresponding recommended path according to the node state; s7, optimizing a model structure and initial parameters through a black widow spider optimization algorithm; and S8, outputting diagnosis and treatment suggestions and reasoning paths. According to the method, intelligent organization of medical knowledge and accurate diagnosis and treatment path recommendation are realized, and the auxiliary decision making efficiency and reliability are improved.

The invention relates to the technical field of medical information, in particular to a medical data conjoint analysis system based on medical knowledge graph driving. The system comprises a medical knowledge graph construction module, a medical data quality evaluation module, a medical feature engineering module and a medical knowledge driven analysis module, and heterogeneous knowledge graph modeling can be performed by integrating medical clinical guidelines, biomedical literatures, a drug database and historical medical data so as to generate a medical heterogeneous knowledge graph; obtaining new medical clinical test information and carrying out delayed contradictory learning update to generate a medical dynamic update knowledge graph; the method comprises the following steps: obtaining multi-modal medical data, carrying out quality verification evaluation and medical feature engineering analysis, carrying out knowledge path joint driving analysis at the same time, generating a decision support reasoning path corresponding to medical clinical knowledge, and outputting a corresponding medical knowledge path confidence coefficient. According to the method, fusion reasoning among cross-source data can be realized by constructing the multi-modal medical knowledge graph.

The invention discloses a semantic enhancement auxiliary inquiry system and method based on a medical knowledge graph, and the system comprises a patient information receiving module, a theoretical knowledge graph storage library, an evidence-based knowledge graph storage library, a double-track diagnosis path construction module, a medical knowledge conflict judgment module, and a semantic enhancement report generation module. The double-track diagnosis path construction module inquires the received patient information in a theoretical knowledge graph storage library and an evidence-based knowledge graph storage library which are independent from each other in parallel, and a theoretical diagnosis path and an evidence-based diagnosis path are generated respectively; the medical knowledge conflict judgment module dynamically compares the two paths in real-time interaction, and performs priority judgment according to a preset medical judgment rule; finally, the semantic enhancement report generation module presents the two paths and the conflict judgment result to the user at the same time. Transparency and interpretability of the whole interrogation process are assisted, and reliable semantic enhancement decision support is provided for doctors when the doctors face complex medical knowledge conflicts.

The invention relates to the technical field of medical decision, and discloses a medical decision support system based on a knowledge graph, and the system comprises a knowledge graph construction module which constructs an initial knowledge graph based on a medical ontology library, and the knowledge graph comprises entities and association relationships of diseases, symptoms and drugs; the data acquisition module is used for acquiring data from an electronic medical record, wearable equipment, a medical literature library and a hospital information system and normalizing the data through a standardized protocol; the dynamic knowledge updating module is used for processing normalized data through an incremental graph neural network; a multi-source knowledge fusion module; a context awareness module; a dynamic deduction module; and a decision optimization closed loop module. And triggering a preset clinical rule in real time based on the pathological state of the patient, dynamically adjusting the intensity value of the related edge in the factor graph, and persistently storing the intensity value back to the knowledge graph, so that logic adaptation and individualized experience precipitation of general medical knowledge in a special pathological state are realized, and the individualized treatment accuracy is ensured.

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.

Provided in the present invention are a method and system for real-time update of a medical knowledge base, and a medium and a device. The method comprises: acquiring medical literature data in real time; analyzing the medical literature data, so as to obtain first structured data; performing correlation analysis on the first structured data and second structured data in a medical knowledge base, so as to obtain an analysis result; on the basis of the analysis result, determining whether to update the medical knowledge base; identifying the differences of the medical knowledge base before and after the update, so as to obtain a difference identification result; assigning a unique version number to each difference in the difference identification result, so as to obtain version update records; and on the basis of the version update records, using incremental hash table technology to perform data storage for each difference. The present application has a higher update efficiency without the risk of omission; and by means of difference analysis and by using incremental hash table technology to store difference data, the present application significantly reduces data storage space requirements, and also improves data access speed.

The invention discloses a cerebral apoplexy multi-mode early screening intelligent evaluation system based on a large model, and relates to the technical field of medical health information, the cerebral apoplexy multi-mode early screening intelligent evaluation system comprises an intelligent management platform, and the intelligent management platform is in communication connection with the following modules: a multi-source heterogeneous data fusion engine, the data integration module is used for integrating multi-modal data including clinical data and terminal health data and constructing a health portrait of a patient; and the cerebral apoplexy knowledge graph construction platform is used for constructing a cerebral apoplexy domain knowledge graph in combination with evidence-based medical knowledge. By combining the digital twinning technology and the intelligent risk assessment engine, the influence of different intervention schemes on the cerebral apoplexy risk can be simulated, personalized intervention suggestions are generated, a patient is helped to reduce the cerebral apoplexy risk and change from passive prediction to active intervention, the patient is helped to take effective measures earlier, the health condition is improved, and the patient experience is improved. The occurrence of cerebral apoplexy is prevented, so that the disability rate and the death rate caused by cerebral apoplexy are reduced.

The invention provides a brain tumor survival prediction method and system based on a multi-modal medical knowledge graph, and belongs to the technical field of brain tumor survival prediction. The multi-modal medical knowledge graph based on third-party knowledge base fusion is constructed; performing feature extraction on the brain tumor multi-modal data; searching an entity corresponding to the brain tumor related data in the multi-modal medical knowledge graph, and converting the entity into feature representation by using an entity representation learning method; the learned feature representation related to the brain tumor type complements the missing data mode, and finally the complemented features are input into a pre-trained survival prediction model to achieve brain tumor survival prediction. According to the multi-modal medical knowledge graph, comprehensive medical knowledge support meeting clinical requirements is provided; the multi-modal mapping knowledge domain is used for missing modal completion of brain tumor survival prediction, and a completion feature is generated by querying an associated entity through the mapping knowledge domain, so that the problem of weak modal missing processing capability in the prior art is solved.

The invention discloses a medical data structured extraction method based on machine learning, and the method comprises the following steps: carrying out the standardization processing of multi-source heterogeneous data in different medical scenes, constructing a time and condition two-dimensional filtering rule, and extracting preliminary data; and a modular index structure is formed according to medical process and technical attribute division. And generating analysis limiting conditions by fusing the medical knowledge graph and the knowledge base, guiding an analysis engine to perform semantic routing and reasoning, and outputting a structured result. Finally, disease identification and quality judgment are achieved, and structured information meeting or not meeting the standard is output. The method aims at efficiently extracting the structured information from various types of medical documents.

The invention discloses a systemic lupus erythematosus assessment method and device based on multi-modal medical map and medical knowledge fusion, terminal equipment and a medium, and relates to the technical field of medical artificial intelligence. The method comprises the following steps: acquiring multi-dimensional medical indexes of a patient, and constructing a multi-modal medical knowledge graph of the patient associated with the indexes; pre-training the self-supervised graph neural network to obtain a patient and medical index embedding vector; grouping and constructing a feature subset combination according to medical knowledge, and training and selecting an optimal sub-model; dynamically weighting and fusing the prediction probabilities of the sub-models to obtain a comprehensive prediction probability; and screening the key indexes based on the embedded vector and quantifying the activity evaluation contribution degree of the key indexes to obtain an activity comprehensive score. The systemic lupus erythematosus evaluation method improves the accuracy and robustness of preliminary evaluation of systemic lupus erythematosus, solves the problems of data missing and sample imbalance, and is explainable in output evaluation, adaptive to primary medical treatment and capable of supporting hierarchical diagnosis and treatment.

The invention discloses a medical knowledge question-answering system based on an RAG (Retrieval-Augmented Generation) architecture, relates to the field of artificial intelligence and natural language processing, and specifically comprises an input representation module, a knowledge retrieval module, a context construction module and a generation reasoning module. The input representation module is used for encoding user query and medical knowledge entries into high-dimensional dense vectors and comprises a semantic encoder; the knowledge retrieval module comprises a sparse retrieval unit, a dense retrieval unit and a fusion sequencing unit; the context construction module comprises a feature splicing unit and a code fusion unit; and the generation reasoning module calls a large language model based on the fused context to generate medical question and answer content in a natural language form. According to the system, by introducing the structured medical knowledge base and a mixed retrieval mechanism, the accuracy and specialty of questions and answers are effectively improved, the language model illusion phenomenon is reduced, the knowledge updating capacity is enhanced, and the system is suitable for application scenes such as clinical consultation and intelligent medical questions and answers.

The invention provides a medical decision-oriented multi-level knowledge graph construction and semantic reasoning method, and relates to the technical field of knowledge graphs, and the method comprises the steps: carrying out semantic segmentation and standardization processing on medical text corpora, extracting medical entities and attributes thereof, and constructing an incidence matrix; a reasoning path is mined based on recursion deep search, and an optimal path is selected by using an attention mechanism; and performing decision verification and optimization in combination with medical rules. According to the method, the accuracy and reliability of medical decision making can be improved, efficient mining and application of complex medical knowledge are achieved, and effective support is provided for clinical diagnosis and treatment.

The invention discloses a medical insurance intelligent auditing and path recommending method and system based on a large language model, and particularly relates to the technical field of medical insurance auditing, and the method comprises the steps: S1, analyzing massive medical insurance policy documents through a first LLM, and generating a comprehensive confidence coefficient in combination with triple confidence coefficients of semantics, context support degree and timeliness, after grading processing, constructing a dynamically updated medical insurance rule knowledge base; s2, converting free text medical advices of doctors into structured data by means of a second LLM, quantifying the integrity of the medical advices through field weights, and dynamically asking and complementing information in combination with constraint urgency; s3, associating the diagnosis and treatment scheme with the medical insurance information of the patient, matching rules in real time, and generating a color identifier for early warning violation; s4, recommending compliance alternative schemes in combination with the medical knowledge graph, and sorting according to multi-dimensional scores such as medical insurance economy and the like; and S5, automatically extracting multi-system data, and generating a standardized medical insurance declaration report. The medical insurance management efficiency is improved, the burden of medical care and medical insurance departments is relieved, and reasonable use of medical insurance funds is guaranteed.

The invention provides an anesthesia virtual simulation training system fusing knowledge, skills and a thinking closed loop. The anesthesia virtual simulation training system comprises a medical knowledge base module, a clinical thinking module, a skill training module, an examination question brushing module, a knowledge graph module and an intelligent platform bottom layer framework. The intelligent platform underlying architecture comprises a data middle platform, an AI engine and a 3D engine, collects student behavior data of each module, constructs a dynamic student ability portrait through a gradient boosting tree algorithm and a collaborative filtering recommendation model, analyzes knowledge blind areas and skill shortages, plans a personalized learning path and pushes targeted training content, and provides a personalized learning result. A closed-loop process of evaluation, learning, practice and re-evaluation is formed; and deep fusion of theoretical knowledge, clinical thinking and skill operation is realized through a cross-module collaboration mechanism. The problems that traditional anesthesia teaching is high in practical operation risk, scattered in resource and insufficient in individuation are solved, and the clinical comprehensive ability and teaching quality of anesthetists are effectively improved.

The invention discloses a knowledge graph construction method and device based on multi-modal clinical data, equipment, a medium and a product, and relates to the field of knowledge graph construction, and the method comprises the steps: constructing a medical knowledge type knowledge graph; based on the dynamic event data of the patient and the doctor-patient dialogue data, constructing a medical event type knowledge graph; fusing the medical knowledge type knowledge graph and the medical event type knowledge graph to obtain a fused medical knowledge graph; collecting multi-modal data in the diagnosis process of the patient, and fusing the multi-modal data with the fused medical knowledge graph to obtain a finally constructed knowledge graph; the multi-modal data comprises text data, image data, voice data and physiological signal data. The problems that clinical knowledge is fragmented, deep mining is difficult, data cannot be associated in a unified mode, and doctor-patient communication knowledge is fragmented can be solved.

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 provides a bone tumor fine-grained classification model training and classification method and device, and the method comprises the steps: constructing a multi-modal positive sample containing global / local positive lateral X-ray images, lesion attributes and patient information, and removing a false negative part in combination with text semantic similarity to construct a high-quality negative sample; global / local image features are extracted through double image encoders, lesion attribute keywords are converted into'entity-translation-existence 'triples based on a medical knowledge base, and basic information of a patient and global / local semantic features of lesion attributes are extracted through a text encoder; infoNCE contrast loss is constructed for global images and global semantics based on contrast learning, global image-text feature alignment and local image-text feature alignment are realized in combination with a local mutual information loss and classification loss training model calculated based on a DV variational formula, medical term semantics are deeply combined, the training stability is improved, and the training efficiency is improved. The accuracy and robustness of bone tumor subtype classification are remarkably improved, and reliable support is provided for clinical precise diagnosis.

The invention relates to an intelligent medical record generation and quality control method and system based on a large model, belongs to the technical field of medical information and artificial intelligence, and solves the problems of low writing efficiency and non-uniform quality of medical records in the prior art. Comprising the following steps: constructing a medical field knowledge enhancement large model which has the capabilities of intention recognition, medical record generation, term standardization check, format integrity check, information extraction and logic consistency check; when input information of a to-be-generated medical record is received, generating a complete medical record through intention recognition and medical record generation capability of the medical field knowledge enhancement large model and the constructed medical record template library and medical database; and when a medical record to be subjected to quality control is received, quality control suggestions are generated through the capabilities of term standardization check, format integrity check, information extraction and logic consistency check of the medical field knowledge enhancement large model and the constructed medical knowledge graph. And efficient generation and quality control of the medical record are realized.

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 discloses a medical review and publication quality control system based on deep learning and medical knowledge, and relates to the technical field of medical information processing, and the system comprises a medical knowledge enhancement module which is used for carrying out the named entity recognition and relation extraction technology based on a multi-source fusion medical knowledge system, constructing a structured medical knowledge graph and carrying out intensive training; the multi-dimensional examination and analysis module is used for performing multi-dimensional evaluation on the manuscript based on the medical knowledge graph; the intelligent interaction and feedback module is used for displaying the evaluation result of each dimension through a visual interface; and the publishing quality management and control module is used for sequentially executing whole-process publishing supervision of standardized monitoring, quality inspection and abnormal intervention on the manuscripts passing the examination and analysis. The manuscript reviewing efficiency is greatly improved, and the requirements of high-frequency and rapid publishing of medical scientific research are met; the problem of review difference caused by subjective factors of a manuscript reviewer is solved, and the stability and reliability of the assessment result of the same manuscript are ensured through a standardized and quantitative assessment system.

The invention discloses a Chinese electronic medical record data processing method and system based on a large language model. The method comprises the following steps: acquiring a Chinese electronic medical record, and performing data preprocessing to obtain a preprocessed Chinese electronic medical record; inputting the preprocessed Chinese electronic medical record into a multi-agent based on the fine-tuned large language model, and combining the multi-agent with the input data according to different processing tasks of the multi-agent to obtain a first cue word; retrieving a medical extraction knowledge base according to the first cue word to obtain corresponding medical knowledge, and combining the first cue word to obtain a second cue word; each agent calls the fine-tuned large language model according to the corresponding second cue word, information extraction and standardization processing are carried out, and extracted medical information is output. According to the method, the accuracy of medical entity and attribute extraction can be improved, combined extraction of complex medical entities and attributes thereof is achieved, standardized mapping of medical terms and time expressions is completed, and the term ambiguity and fuzzy time analysis problems are solved.

The invention provides a medical information retrieval enhancement method and device based on a large model and related equipment, and relates to the technical field of artificial intelligence. The method comprises the following steps: in response to a medical information query request, executing double-path joint retrieval of keywords and semantics in a pre-constructed medical knowledge base to obtain a candidate data set containing at least one medical information query result, the medical knowledge base is a database which analyzes the multi-modal medical data based on a multi-modal medical data deep analysis large model and is constructed according to the analyzed medical text data; and adopting a plurality of sorting tools to resort the medical information query results in the candidate data set, and performing fusion processing on a plurality of resorting results obtained by resorting to obtain a medical information query result after retrieval enhancement. According to the method and the device, the precision improvement and response efficiency optimization of medical knowledge retrieval can be realized, a high-credibility knowledge enhancement service is provided for a clinical decision support system, and the method and the device have important application value.

The invention discloses a medical diagnosis-oriented multi-modal credible dialogue type retrieval enhancement generation system, and relates to the field of artificial intelligence, in the system, a multi-modal image analysis module generates a structured image feature data packet based on a multi-modal large model according to an original medical image; the knowledge database construction module is used for constructing a multi-center collaborative medical knowledge database; the image knowledge retrieval module is used for obtaining image feature associated knowledge; the clinical knowledge retrieval module is used for obtaining a candidate knowledge set; a multi-dimensional weighting reordering module obtains a knowledge list; the medical inquiry generation module is used for generating structured preliminary diagnosis based on the user questions, the image abstract and the knowledge list; the credibility verification module is used for verifying the structured preliminary diagnosis to obtain a final diagnosis report; according to the method, the medical knowledge retrieval precision can be improved, the image information fusion capability is enhanced, and high-credibility and traceable intelligent diagnosis and treatment auxiliary service oriented to doctor-patient scenes is realized.

The invention discloses a medical informatization data intelligent analysis system based on a large model, and belongs to the technical field of medical informatization and artificial intelligence, and the system comprises a data collection and standardization module, a medical knowledge graph construction module, a knowledge enhancement inference analysis module, a data quality evaluation module and a structured output module. The system collects multi-source heterogeneous medical data from an electronic medical record system, a laboratory information system, a medical image information system and a hospital information system, performs standardization processing, automatically constructs a medical knowledge graph, and performs intelligent analysis and reasoning on the medical data by using a knowledge retrieval enhanced medical field large language model. Meanwhile, the data quality is evaluated in four dimensions of integrity, accuracy, timeliness and relevance, and finally a structured analysis report is generated. The multi-source medical data can be effectively integrated, the accuracy and interpretability of medical data analysis are improved, and intelligent support is provided for clinical decision making.

The invention relates to the technical field of medical knowledge retrieval, in particular to a traditional Chinese medicine acupuncture field knowledge retrieval method and system based on deep learning, and the method comprises the following steps: obtaining an acupoint sequence and combining with semantics to construct a vector fragment, extracting symptom-related acupoint analysis response to generate a path sequence, and extracting a root combination vector matrix; according to the method, the sequence information of the acupuncture points in the meridian path is extracted, the semantic description and the major content are combined to construct the structured expression, the path organization and recognition capability is enhanced, the curative effect level and the treatment cycle are fused to form the sequence, the response pertinence of the acupuncture points is improved, and the method has the advantages of being simple in structure, convenient to operate and high in practicability. Through major treatment, direction and positioning root three-dimensional combination vectorization expression, semantic comparison and standardization are enhanced, keyword matching frequency and distribution density are combined, semantic association between input and knowledge is enhanced, and acupoint combinations are screened based on semantic matching and path continuity, so that the result is ensured to have relevance and practicability.

The invention belongs to the technical field of medical data processing, and particularly provides a multi-mode heterogeneous medical data dynamic weighting intelligent disease analysis system. The system specifically comprises the following modules: a multi-modal heterogeneous data preprocessing module, a multi-modal data quality dynamic evaluation and quality control module, a cross-modal embedding space conversion network, a medical knowledge graph engine, a large model training architecture, a multi-modal decision fusion module, a disease analysis interpretability enhancement module and a Bayesian probability graph model integration module. And an edge cloud collaborative reasoning module. According to the invention, the accuracy and robustness of multi-modal disease analysis are improved, the adaptability quality control of different modal data is realized through the multi-dimensional dynamic quality evaluation module, and the reliability of basic data is improved in combination with intelligent quality control restoration.

The invention relates to the technical field of knowledge fusion, and discloses an interactive health science popularization question and answer guidance system for constructing a medical knowledge graph, comprising: a guidance instruction generation module extracting attribute variation dimensions between to-be-aligned data and candidate nodes and generating a guidance instruction; the feedback analysis module converts the feedback data into a feedback correction vector; the atlas fusion actuator determines an initial fusion weight according to the coordinate distance, determines a weight correction coefficient based on the topological centrality of the target node, constructs a weighted feature update function by using a feedback correction vector and a local topological relation to calculate a feature increment, and completes feature convergence fusion; according to the method, a priori constraint force balance feedback feature pointing value provided by a map structure is utilized, it is ensured that heterogeneous knowledge fusion conforms to medical ontology logic, and the global stability of science popularization map evolution is guaranteed.

The invention discloses a personalized health management method and system based on an AI electronic medical record, and relates to the technical field of artificial intelligence medical treatment, and the method comprises the steps: analyzing an original electronic medical record to generate a personal health timeline; carrying out feature extraction on the health state evolution sequence, identifying key nodes, and carrying out pathological labeling according to a medical knowledge graph to form a health state evolution sequence with a pathological label; a risk assessment model is constructed, future disease risks are calculated based on the sequence, and a dynamic report is generated; making a personalized health management plan in combination with the living habits and genetic backgrounds of the users; during plan execution, user feedback and monitoring data are collected in real time, and plan content and strength are dynamically adjusted by using a reinforcement learning mechanism. According to the method, the medical interpretability of health state evolution is enhanced through pathological labeling, and dynamic closed-loop optimization of a management plan is realized through reinforcement learning.

The invention relates to the technical field of medical information processing, and discloses an intelligent prescription auxiliary issuing system and method based on generative artificial intelligence and a storage medium, and the system comprises a multi-modal input module, a model generation module, a knowledge integration module, a prescription generation module, an auditing optimization module and a prescription output module. The method corresponds to the system, and the storage medium corresponds to the method. According to the application, the limitation of function fragmentation is avoided, diagnosis and treatment instructions, personalized habits of doctors, dynamic medical knowledge and risk control requirements are cooperatively and comprehensively planned through the modules, a medication safety base line is kept through risk grading verification, optimization suggestions are generated according to diagnosis and treatment habits of the doctors, meanwhile, the accuracy and efficiency of prescription making are improved, and the application prospect is wide. The comprehensive requirements for safety, personality and high efficiency of prescription assistance in clinical scenes are fully met, powerful support is provided for diagnosis and treatment work of doctors, and medication safety and treatment effects of patients are indirectly guaranteed.