1562 results about "Medical record" patented technology

The invention relates to the technical field of text data analysis, in particular to an electronic medical record free text analysis method, system and device, which improve the efficiency of information extraction and reduce the demand of manual intervention. The method comprises the steps of receiving free text data of the electronic medical record, performing cleaning, word segmentation and medical term standardization processing, and converting an unstructured text into structured data; based on deep learning and a medical knowledge base, text features are extracted through a pre-training language model, entity boundaries are captured on an output layer in combination with a conditional random field, medical entities in a text are recognized, and the recognized entities are classified and labeled; extracting a causal relationship, a treatment relationship and an examination relationship among entities through a dependency syntactic analysis and semantic role labeling technology, and constructing an entity association network; performing dynamic correction and supplementation on entity classification and relationships in combination with medical record context information; and outputting an analysis result in a structured JSON format to generate a computable semantic map.

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 provides a patient information collection and medical record construction system and method based on multiple rounds of dialogues, and the system comprises an intelligent guide interaction module which receives the natural language input of a patient; the context-aware question and answer engine adopts a dialogue state representation method based on a graph structure to construct entities, relationships and attributes of each round of dialogue into knowledge sub-graphs; the medical record information dynamic builder monitors an updating event of the dialogue state diagram in real time; the abnormal information detection module adopts a mixed conflict detection method combining rules and learning; and the medical knowledge graph support system maps the oral expression of the patient to a standard medical term system in real time through a multi-level semantic matching strategy. According to the invention, the doctor does not need to distract the record in the inquiry process, and can pay more attention to patient observation and clinical thinking. The quality of the medical record first draft automatically generated by the system is high, a doctor only needs to perform a small amount of auditing and supplementing, and the medical record writing time is greatly shortened.

The invention relates to the field of medical informatization, and discloses an intelligent medical record generation method and system for realizing non-sensitive experience, and the method comprises the steps: carrying out the real-time dynamic capturing of a whole patient reception process, and extracting a patient physiological parameter boundary, a doctor seeing interaction node and a diagnosis and treatment environment noise region based on a medical scene semantic segmentation algorithm; acquiring a diagnosis and treatment state multi-source data stream sequence; performing cross-modal time sequence deep association matching on the diagnosis and treatment state multi-source data stream sequence; based on the multi-dimensional dynamic feature matrix, utilizing an adaptive semantic evolution clustering algorithm to extract potential semantic offset paths in a medical record generation process, and identifying high-risk information distortion candidate nodes by detecting distribution abnormity of semantic evolution trajectories; dividing the diagnosis and treatment semantic units and the influence intervals corresponding to the high-risk information distortion candidate nodes as candidate correction areas; and generating a map based on the local medical record, and performing intervention decision on the information evolution link. The method has the advantage of improving the experience feeling of the patient.

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 provides a medical auxiliary diagnosis method and system based on time sequence and semantic weighting, and belongs to the technical field of medical information processing. Constructing the preprocessed current and historical medical record information of the patient into patient medical record data, and inputting the patient medical record data into a pre-trained medical language model to generate a preliminary diagnosis result; the comprehensive weight of the patient medical record data vector sequence is calculated through a time decay function and the content correlation weight, a medical record fusion vector is obtained through fusion, and the medical record fusion vector and the preliminary diagnosis result are spliced into a query vector; searching candidate fragments in a clinical guide knowledge base based on the query vector, calculating semantic evidence scores and coverage scores based on patient medical record data and tokens of the candidate fragments, obtaining sorting probabilities of the candidate fragments in combination with metadata prior scores of the candidate fragments, and screening out target guide fragments; and the preliminary diagnosis result and the target guide fragment are fused to generate final auxiliary diagnosis and treatment information, so that auxiliary diagnosis and treatment suggestions with traceability, verifiability and authoritative basis are provided.

The invention discloses a traditional Chinese medicine famous prescription and operation multi-agent collaborative consultation method and system. The system comprises a medical case knowledge base module, an expert knowledge base module, a medicine knowledge base module, a pre-differentiation module, a multi-agent diagnosis module and the like. The method comprises the following steps: constructing structured experience medical case data, an expert experience knowledge graph and a traditional Chinese medicine knowledge graph knowledge base, carrying out mixed retrieval to carry out pre-differentiation, calling multi-agent consultation according to preliminary disease differentiation and syndrome types, and obtaining an expert diagnosis scheme for risk verification and prescription adjustment optimization. According to the invention, a multi-agent collaborative diagnosis technology is utilized, and a multi-agent architecture for simulating real traditional Chinese medicine consultation is constructed. A plurality of expert agents perform independent diagnosis and complement each other, so that the limitation of a single agent is avoided, and comprehensive and accurate diagnosis is provided. And in combination with expert experience knowledge graph and big language model reasoning, the system can flexibly differentiate symptoms according to the symptoms of the patient, accurately judge disease types and syndrome types, and improve the differentiation accuracy.

The invention discloses an electronic medical record LLM generation method based on animal injury, which realizes dialogue structuring and timestamp synchronization through multistage speech recognition and role affiliation. Using standardized medical term mapping and coding to align the free text to a standardized medical entity, and constructing a high-confidence medical entity network based on a semantic anchor point pool; according to the method, context-sensitive entity relationship extraction is realized by combining a large language model and a semantic enhancement template, a high-accuracy structured relationship chain is generated through clinical logic rule set verification, and finally, an electronic medical record template under diagnosis and treatment specifications is automatically filled and privacy desensitization processing is completed. The semantic consistency, the structural accuracy and the data security of automatic generation of the electronic medical record are improved, and standardization and intelligent circulation of medical information are effectively promoted.

The invention provides an electronic medical record information extraction system and method, and belongs to the technical field of medical information extraction. The method aims to solve the problems of inaccuracy, nonstandardization and inconsistent logic when information is extracted from a free text medical record. According to the scheme, the method comprises the steps that cue words containing structured field definitions and rules are generated; inputting the cue word and the medical text into a large language model to obtain a preliminary result; calling a medical knowledge base to correct terms in the result; and performing logic verification on a corrected result, including time sequence and cross-field consistency verification, so as to generate final structured medical record information. According to the method, a closed loop is formed by guiding type extraction and multi-stage verification and by introducing a feedback correction mechanism, so that the accuracy, the normalization and the logic consistency of the medical record information are remarkably improved, and a high-quality data basis is provided for clinical application.

The invention discloses an auxiliary film reading method and system based on artificial intelligence, and the method comprises the steps: 1, collecting a pathological WSI, an electronic medical record, detection data and equipment parameters, correcting the equipment difference through adaptive dyeing normalization, and constructing a structured data package associated with an ID-timestamp of a patient; 2, developing a dynamic branch CNN, migrating teacher model knowledge through knowledge distillation, and introducing federated learning; step 3, the edge generates a thermodynamic diagram to mark a suspicious area, and the cloud outputs a structured report; step 4, constructing a normal tissue feature space by the variational auto-encoder, detecting abnormal slices and triggering expert re-checking; a reverse automatic encoder generates a pseudo-health image to compare and position a pathological area, and dynamic weight adjustment balances the federal learning convergence speed; 5, integrating the thermodynamic diagram, the gene data and the clinical indexes by a three-dimensional platform, and supporting multi-dimensional superposition display; webGL realizes browser end rendering, and NLP automatically generates a report abstract marked with a key evidence chain and is in butt joint with an international diagnosis and treatment guide.

The invention provides a multi-modal knowledge graph construction method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining a traditional Chinese medicine text; according to the traditional Chinese medicine text, candidate triples are generated through a large language model; generating a structured definition according to entities and relationships in the candidate triple; according to the structured definition, performing standardization processing on the candidate triad to obtain a standardized triad; according to the standardized triple, constructing an initial multi-modal knowledge graph by integrating multi-modal data; according to the initial multi-modal knowledge graph, constructing a theoretical layer knowledge graph through a neighbor perception filtering strategy; updating the theoretical layer knowledge graph through a dynamic knowledge updating mechanism; acquiring medical record data; constructing a real-world medical record layer knowledge graph according to the medical record data; and fusing the updated theoretical layer knowledge graph and the real-world medical record layer knowledge graph through an entity alignment mechanism and confidence fusion to form a multi-modal knowledge graph.

The invention provides a multi-mode medical record intelligent generation and interaction system and method based on a wearable terminal and an MOE architecture, and relates to the technical field of AI large models. The terminal comprises a wearable terminal used for carrying out real-time acquisition and interaction on multi-modal data in a medical scene to obtain multi-modal medical data; the communication network is used for realizing data interaction between the wearable terminal and the server-side AI large model processing system; the server side AI large model processing system is used for performing semantic analysis, structured extraction and medical record document generation based on the multi-modal medical data to obtain an editable first draft of a medical record document; and the wearable terminal is also used for receiving and presenting the first draft of the medical record document returned by the server side AI large model processing system, and editing and confirming the first draft of the medical record document to form a returned draft of the medical record document. The problem that in the prior art, a terminal lacks multi-modal real-time fusion and LLM closed loop, and doctor duplication interaction cannot be achieved is solved.

The invention relates to the technical field of data processing, and discloses an old people health state assessment data processing system based on multi-modal data, and the system comprises a medical data integration module which obtains electronic medical record data and a medical examination report through an FHIR interface, and extracts a structured health index; the cross-modal causal fusion processing module is used for fusing the monitoring data and the medical text through an image, text and image interlayer architecture; the health state evolution modeling module is used for mapping the health feature vectors into physiological function, cognitive level and athletic ability three-dimensional state indexes; an evaluation report backtracking module; and a decision output module. Through an image, text and image interlayer architecture, deep semantic fusion of multi-modal features is realized under the constraint of medical pathology rules, feature weight adaptive distribution is dynamically guided based on a medical causal atlas, a high-dimensional fusion vector retaining key pathology information is generated, the semantic integration ability of health data is remarkably improved, and the health data fusion efficiency is improved. And the reliability of discrimination and decision making is improved.

The invention provides a man-machine collaborative intelligent writing method and system for medical documents, and relates to the technical field of man-machine collaboration.The method comprises the steps that multi-modal clinical data of a patient is obtained for cross-modal semantic alignment, reasoning expansion is conducted based on a multi-level traditional Chinese medicine knowledge graph to generate a structured dialectical framework, and the structured dialectical framework is subjected to data processing; a logic consistency verification graph of a candidate medical record fragment set is constructed to solve semantic conflicts, and a medical record generation model is dynamically optimized in combination with clinical actual curative effect data, so that the intelligence degree, the standardization level and the clinical diagnosis and treatment value of traditional Chinese medicine medical record writing are improved.

The invention relates to the technical field of intelligent diagnosis and treatment, and discloses a remote intelligent disease diagnosis and treatment system and method based on a block chain. The method comprises the steps that multi-modal physiological data such as real-time vital signs, historical medical record texts and medical images of a patient are collected, an encrypted data packet is generated, distributed storage is conducted through a block chain node network, and a data hash value and a timestamp are generated; and calling an intelligent contract to extract cross-modal features, generating a standardized feature vector set containing a multi-feature association identifier, and constructing a diagnosis and treatment decision tree capable of dynamically adjusting branch paths according to the standardized feature vector set. Verifying decision validity through a consensus mechanism, and generating a treatment instruction sequence containing drug configuration, instrument operation and a follow-up visit period; and monitoring feedback data in real time and updating the feedback data to the block chain, triggering an intelligent contract to generate a correction scheme when the deviation exceeds a threshold value, and writing the difference data as a new transaction record into the block chain, thereby realizing multi-modal data security management and control and dynamic precise diagnosis and treatment.

Described herein are a system, method, and device for configuring a generative machine learning model using a syntactic interface. A system may include a user interface, a memory, and a processor configured to, using a syntactic interface displayed using the user interface, receive a syntactic interface input from a user; identify an electronic medical record (EMR) by generating an EMR database query as a function of the syntactic interface input, querying an EMR database using the EMR database query, and receiving, from the EMR database, an EMR database response; generate a prompt as a function of the syntactic interface input, generate a first generative model output as a function of the prompt and the EMR using a trained generative machine learning model and using a conversational interface displayed using the user interface, display the first generative model output to the user.

The invention discloses an intelligent auxiliary diagnosis system and method based on big data analysis, and relates to the field of intelligent auxiliary diagnosis. Original CT scanning data and electronic medical record data of a patient are processed in parallel, and a deep learning model is introduced to extract high-dimensional image features and text features; through constructing a knowledge prior relation matrix derived from clinical guidelines and expert rules, knowledge-guided cross-modal attention fusion is carried out on image features and text features so as to realize deep and accurate joint characterization of patient conditions, and then intelligent risk prediction and classification are carried out on the basis of the deep and accurate joint characterization. Thus, through deep coupling of data-driven feature learning and medical knowledge-driven logical reasoning, intelligent and logical integration of multi-modal medical information can be realized, and the accuracy and reliability of an auxiliary diagnosis model in a complex clinical scene are effectively improved.

The invention discloses a pressure injury risk early warning method and equipment, and relates to the technical field of medical monitoring instruments. The pressure sensor array layout can be self-adaptive to the body shape of a patient, a key osseous process part is covered, no monitoring dead angle is ensured, a temperature compensation mechanism is built in, environmental interference is reduced, the measurement precision is improved, an abnormal pressure threshold value can be dynamically set based on individual medical record data, data verification is performed before early warning is triggered, false alarm is avoided, and the array structure is light, thin and flexible. The mask is comfortable to fit skin, supports long-term wearing and is easy to disinfect and maintain; environment temperature and humidity parameters and pressure data are subjected to fusion analysis, the influence of a high-temperature and high-humidity environment on skin microcirculation can be recognized, the risk of pressure sores can be evaluated in an auxiliary mode, a calibration algorithm is built in a sensor, environment fluctuation interference is reduced, and data reliability is ensured; and the early warning unit comprises sound-light alarm equipment which supports a multi-stage alarm mode and is used for triggering an early warning signal and pushing information when the risk index exceeds a threshold value, so that the early warning timeliness is ensured and the false alarm risk is reduced.

The invention discloses an intelligent medical record quality control method based on a rule and large language model hybrid engine, and relates to the technical field of natural languages, and the method comprises the steps: obtaining original electronic medical record data from a medical information system; performing post-structured processing on the original electronic medical record data, and converting an unstructured or semi-structured text in the original electronic medical record data into a unified structured data object readable by a machine; traversing a plurality of quality control points in a preset quality control rule base, and for each quality control point, adaptively selecting and executing a corresponding quality control path based on the converted structured data object; and based on the quality control results of all the quality control points, performing score calculation and generating a quality control report. According to the method, the automatic and deep quality control of the full-dose medical record can be realized, form inspection is covered, connotation logic analysis can be executed, the system is endowed with excellent flexibility and regional expansion capability, and reliable technical support is provided for improving the overall level of medical quality management.

The invention provides a traditional Chinese medicine electronic medical record natural language processing and knowledge graph construction method and system. The method belongs to the technical field of the crossing field of traditional Chinese medicine informatization, artificial intelligence natural language processing and knowledge engineering, and comprises the following steps: performing multi-granularity semantic unit division on the traditional Chinese medicine electronic medical record to generate traditional Chinese medicine text semantic unit data; constructing a multi-granularity semantic decoupling engine according to the traditional Chinese medicine text semantic unit data so as to construct a traditional Chinese medicine text semantic analysis framework; performing traditional Chinese medicine unstructured text deep analysis according to the traditional Chinese medicine text semantic analysis framework, and performing multi-dimensional semantic feature extraction to obtain traditional Chinese medicine text semantic feature data and medical record time sequence data; through multi-granularity semantic unit division and deep analysis, the unstructured text in the traditional Chinese medicine electronic medical record can be effectively converted into structured semantic data, and the ability to understand and process traditional Chinese medicine clinical information is further improved.

The invention relates to the technical field of electronic prescriptions, in particular to a dynamic electronic prescription generation method and system based on artificial intelligence. The method comprises the following steps: collecting the latest physiological state parameter flow of a patient, carrying out real-time health state evaluation, and generating a personalized patient state map; historical medical records of a patient are extracted, time sequence pathological evolution tracking is carried out, and a pathological evolution trajectory is generated; performing multi-parameter time sequence difference comparison calculation and drug curative effect quantitative evaluation based on the personalized patient state map and the pathological evolution trajectory to obtain a curative effect evaluation report; performing allergic drug identification on the patient based on the historical medical record of the patient, and performing secondary drug screening to obtain a drug candidate set; and performing intelligent matching calculation on the drug candidate set according to the curative effect evaluation report, and performing combinatorial optimization analysis to generate a final effective electronic prescription. The electronic prescription is automatically updated and adjusted based on the state change of the patient, the risk of allergic prescriptions is reduced, and the safety of the prescriptions is improved.

The invention relates to the field of clinical terms and the technical field of digital coding, and discloses a data processing method for ICD coding recommendation of outpatient medical records, and the method comprises the following steps: carrying out the preprocessing of outpatient medical record texts, including cleaning, segmentation, coding standardization and punctuation standardization; carrying out medical entity identification and assertion identification on the preprocessed medical record text by adopting a medical entity identification model and an assertion identification model, generating structured information containing evidence fragment offset, and analyzing unstructured and spoken contents by carrying out preprocessing and medical entity identification and assertion identification on the medical record text, so as to obtain a pre-processed medical record text; key information of negative expression and time sequence dependence is captured, structured and standardized processing of medical entity information is guaranteed, the code error and missing risks in code recommendation are reduced, and the accuracy of medical quality control and medical insurance settlement is improved.

The invention relates to the technical field of medical data processing, in particular to a lung cancer risk early warning system based on remote four diagnosis information, which comprises an acquisition module, a generation module, a calculation module, an early warning module and a correction module. According to the method, through multi-modal time sequence fusion and causal map driven closed-loop regulation and control, the more stable, interpretable and adaptive early warning capability for the lung cancer risk is realized, heterogeneous evidences from observation, listening and interrogation are aggregated into a dynamic alarm index according to time and causal paths, instantaneous noise is filtered by using trend consistency and adversarial test, and the early warning effect is improved. Therefore, false alarms caused by short-time fluctuation are remarkably reduced while the sensitivity to real abnormity is kept; the method achieves balance among false alarm reduction, missed diagnosis risk reduction, positive prediction value improvement and user sampling burden optimization, and effectively solves the problem of insufficient early warning accuracy caused by risk identification lag due to dependence on single static medical record information.

The invention relates to the technical field of artificial intelligence, can be applied to the field of financial science and technology and the field of medical treatment, and discloses a question query method, device and equipment based on a self-adaptive series group and a medium, which are applied to a high-frequency quantitative transaction decision scene or an electronic medical record reasoning scene. Generating a complete thinking chain sequence and an initial answer based on the query question; according to a preset truncation position, truncating the complete thinking chain sequence to generate a truncation subsequence and a basic answer, and constructing a series group and an answer set; calculating a correctness index of the answer set and an average correctness rate of the series group; performing dynamic reward switching according to the average accuracy and a preset threshold value to generate a reward set, and calculating a dominant set; updating the initial strategy model according to the advantage set to obtain a target strategy model; and reasoning based on the to-be-reasoned query question through the target strategy model to generate a target answer. According to the method, the query accuracy and efficiency of the multi-step problem query task are improved.

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 provides an AI-based medical record diagnosis and surgical operation code intelligent generation method and system, and relates to the technical field of medical information, and the method comprises the steps: obtaining multi-modal data from a hospital information system, including a text medical record, a medical image, a surgical video and a pathological report; entities and relationships are extracted through a multi-modal fusion model, and a hospital-level disease knowledge graph containing space-time dimensions is constructed; training a coding rule generation model and a verification model based on a reinforcement learning framework, and generating candidate codes and confidence scores thereof; and correcting the candidate codes according to manual feedback, and dynamically updating the knowledge graph and the rule base by using the graph neural network. Accurate disease diagnosis codes and surgical operation codes are generated in an intelligent mode, and the accuracy of the codes can be remarkably improved through automatic medical record code recommendation.

The invention discloses an illness state reasoning and medical record generation method and system based on large model fusion and knowledge enhancement. The method comprises the following steps: acquiring and preprocessing medical data of a patient; performing feature extraction and medical entity recognition on the preprocessed medical data based on a pre-constructed medical entity recognition model to establish a patient state sub-graph; performing topology analysis and multi-hop reasoning on the patient state sub-graph based on a graph neural network to obtain a diagnosis candidate set and a corresponding reasoning evidence chain set; performing double-track parallel illness state reasoning based on the medical data and the reasoning evidence chain set to respectively generate inner track illness state probability distribution and outer track illness state probability distribution, performing weighted fusion on the inner track illness state probability distribution and the outer track illness state probability distribution to generate optimized illness state reasoning probability distribution, and generating a structured medical record with an evidence traceability identifier according to the optimized illness state reasoning probability distribution; according to the method, the problems of logic fracture, knowledge illusion and uninterpretability in large-model medical application are solved, and the accuracy and transparency of clinical diagnosis reasoning are remarkably improved.

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 discloses a trusted data sharing method based on a block chain consensus mechanism. The method comprises the following steps: S1, constructing an alliance chain network; s2, completing KZG polynomial commitment generation, ML-KEM algorithm encryption storage and pointer registration; s3, the data provider medical institution completes an evidence storage transaction of the FROST threshold signature; s4, the target medical institution completes the access request transaction; s5, the data provider medical institution sends a KZG unpacking proof and a data item, the target medical institution obtains a storage pointer and a commitment value, KZG verification check is executed, and after verification is passed, an ML-KEM algorithm is executed for depacking to obtain a session key; and S6, the target medical institution completes the audit transaction, and after the alliance chain network broadcast, the consensus node executes rule verification of the access control contract and the audit contract. The multi-node electronic medical record sharing and tamper-proof evidence storage method is high in performance and traceability, and is suitable for credible circulation of sensitive health data among multiple medical institutions.