1138 results about "Medical knowledge" patented technology

The invention provides a medical text big data intelligent labeling and knowledge graph construction method and system, and relates to the technical field of medical text processing, and the method comprises the steps: recognizing entities in a medical text through a pre-trained deep bidirectional coding model and a multi-level entity recognition network, and carrying out the normalization processing in combination with a medical knowledge base, according to the method, the semantic association degree between entities is calculated, a relation extraction result is optimized based on comparative learning, comprehensive relation representation is obtained through fusion of multi-granularity coding and a gating mechanism, the entity relation is determined in combination with an inference rule network, a medical knowledge graph is constructed based on an initial knowledge graph, and the accuracy of medical text entities and relation labeling can be effectively improved. And a more perfect medical knowledge graph is constructed, which is helpful for assisting medical diagnosis and decision making.

The invention discloses an electronic medical record intelligent evaluation method based on complex quality control indexes, and relates to the field of medical information processing and artificial intelligence. The method comprises the steps that an original electronic medical record text is collected and preprocessed, and structured diagnosis and treatment information and an event sequence diagram are extracted; through prompt word chain construction and a semantic reasoning mechanism, a large language model is guided to intelligently evaluate complex quality control indexes in medical records. The complex quality control indexes comprise diagnosis basis sufficiency, treatment scheme rationality, key result and change record integrity, treatment measure integrity and causal relationship rationality. According to the method, technologies such as a medical knowledge graph, a graph neural network and semantic vector retrieval are utilized to realize external knowledge recall and causal reasoning support; a self-consistency reasoning mechanism, a self-reflection mechanism and a multi-model cross validation mechanism are introduced to improve the accuracy and credibility of an evaluation result; and finally, outputting a structured quality control report and a visual reasoning chain. According to the method, the intelligence and refinement level in a complex medical quality control task can be remarkably improved, high interpretability and practical value are achieved, and the method is suitable for application scenes such as hospital quality management, scientific research evaluation and medical document standardization.

The invention discloses a chronic disease early detection method and system based on a multi-modal large model, and relates to the technical field of intelligent medical treatment and artificial intelligence, and the method comprises the steps: obtaining a multi-modal data stream of a target user in a target time window from a pathology database, and generating an original multi-modal data set; performing timestamp unification and numerical value standardization processing on the original multi-modal data set to obtain a time sequence feature sequence; inputting the time sequence feature sequence to the multi-modal large model to obtain an abnormal symptom feature; calculating the similarity between the abnormal symptom features and feature vectors of marked cases in a historical case library, and determining matched cases; a diagnosis result and a development process of the matched case are extracted, a disease risk level and a development trend corresponding to the original multi-modal data set are determined in combination with the medical knowledge graph, and a pathology assessment result is obtained; and generating an early warning signal containing the risk type and the intervention suggestion according to the pathological assessment result. By implementing the application, the accuracy of early detection of chronic diseases can be improved.

The invention relates to the technical field of artificial intelligence and medical diagnosis, in particular to a disease diagnosis prediction method and system based on a graph neural network. The disease diagnosis prediction method based on the graph neural network comprises the five steps of heterogeneous medical knowledge graph construction, adaptive node embedding representation, hierarchical graph attention network modeling, incremental learning dynamic graph updating and multi-dimensional feature input and result output. The invention discloses a disease diagnosis and prediction system based on a graph neural network. The system comprises a multi-source data acquisition module, a heterogeneous graph construction module, a self-adaptive embedding module, a graph network calculation engine, a dynamic updating module, a disease prediction module and a feedback optimization module. According to the method, the multi-modal heterogeneous knowledge graph is constructed to integrate the multi-dimensional data of the patient, and the hierarchical graph attention network and the dynamic incremental learning are combined, so that the accurate prediction of the disease risk and the visual explanation of the pathological association path are realized.

The invention provides a construction method of a comprehensive intelligent early warning system for a traumatic patient, and relates to the technical field of medical artificial intelligence, and the method comprises the steps: carrying out the processing of multi-modal data through a feature engineering framework, and extracting multi-scale time sequence features and correlation features; carrying out abnormal state identification and complication risk prediction by adopting a deep learning prediction framework, and carrying out feature fusion through an attention mechanism; constructing a medical knowledge graph to obtain vector representation of domain rule knowledge; and performing integrated verification through a bidirectional mapping mechanism to generate a prediction result. According to the invention, accurate early warning of disease development of the trauma patient can be realized, the early warning accuracy and interpretability are improved, and effective support is provided for clinical decision making.

The invention provides a knowledge and data fused medical content image-text generation system and method. Relates to the technical field of digital medical treatment and artificial intelligence. The multi-modal feature extraction module is used for extracting multi-modal features from the multi-modal data; the multi-modal feature fusion module is used for carrying out cross-modal dynamic fusion on the multi-modal features to generate cross-modal high-consistency fusion features; the model training optimization module is used for configuring a soft and hard target loss function and a parameter optimizer; the information analysis and reasoning module is used for carrying out semantic matching on the fusion features, the disease classification and the clinical path based on a dynamic medical knowledge network, and generating disease prediction probability distribution and a semantic reasoning path; and the image-text report generation module is used for generating an image-text report containing a diagnosis conclusion and an interpretation basis according to the prediction probability and the semantic reasoning path. According to the invention, intelligent and precise generation of medical contents can be realized.

The invention discloses a medical intelligent decision-making method based on Deepseek and a time sequence causal knowledge graph, and the method comprises the following steps: 1, constructing an initial static medical knowledge graph, and generating a dynamic time sequence causal knowledge graph; 2, finely adjusting and training the DeepSeek model to enable the DeepSeek model to adapt to the medical field; and step 3, receiving and analyzing the text uploaded by the patient, performing intelligent triage and disease risk prediction, and realizing accurate matching of patient symptoms and target departments and intelligent prediction and early warning of potential diseases. The method aims at providing accurate triage and disease risk prediction for patients, constructing a scientific and efficient medical intelligent decision-making mechanism and optimizing medical resource allocation.

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 neurology nursing potential safety hazard monitoring system and method, and relates to the technical field of medical information safety monitoring, and the system comprises a first collection processing unit which is used for collecting monitoring data of a target patient, inputting the monitoring data into a time sequence deep network for processing, and obtaining risk feature data; the second collecting and processing unit is used for collecting physiological state data of the target patient, constructing a risk assessment rule base based on a medical knowledge graph to analyze the physiological state data, and generating risk probability data; and the risk early warning unit is used for inputting the risk feature data and the risk probability data into a Bayesian fusion network, generating risk early warning information and sending the risk early warning information to the medical care terminal. According to the invention, collaborative analysis of multi-source information is realized through the Bayesian fusion network, the problem of low early warning accuracy of a traditional single monitoring method is solved, and the reliability of potential safety hazard monitoring is improved.

The invention provides a collaborative reasoning method and system fusing a medical knowledge graph and a large model in the technical field of artificial intelligence. The method comprises the steps that S1, medical entities, medical relationships and medical attributes are extracted from a medical data set through a medical information extraction model to construct the medical knowledge graph; s2, monitoring the latest medical information through a medical information monitoring agent so as to update the medical knowledge graph; s3, creating a clinical decision collaborative reasoning model; s4, training and deploying the clinical decision collaborative reasoning model through the medical data set; s5, pushing the clinical decision collaborative reasoning model to a medical terminal through a federal gateway; and S6, the medical terminal inputs the query appeal carried by the query request into the clinical decision collaborative reasoning model to obtain a reasoning report. The method has the advantages that the reasoning ability, timeliness, interpretability and safety of medical reasoning are greatly improved.

The invention relates to a multi-modal large model dynamic compression and reasoning optimization method based on a MoE architecture. The method comprises the following steps: establishing an edge computing system conforming to medical equipment specifications, constructing a medical image analysis network based on an improved hybrid expert MoE architecture, and adopting a three-layer cascade structure of a feature coding layer, a dynamic routing layer and an expert execution layer; executing expert module dynamic loading and video memory optimization; executing knowledge graph compensation and domain knowledge injection; executing hardware instruction level optimization and calculation acceleration; executing multi-expert feature fusion and decision weighting; performing diagnosis result generation and confidence evaluation; performing real-time data return and model iterative optimization; executing multi-device cooperation and load balancing; executing system security monitoring and exception handling; and generating a structured diagnostic report. The problem that the precision loss of a multi-modal large model is difficult to meet actual requirements is solved, and medical feature adaptive dynamic compression, medical hardware collaborative energy efficiency optimization and cross-modal compensation of medical knowledge enhancement are realized.

The invention provides a potential relation reasoning-based medical knowledge graph retrieval system and a potential relation reasoning-based medical knowledge graph retrieval method. The system comprises a medical data preprocessing module, a knowledge extraction and medical graph generation module, a medical knowledge graph potential relation reasoning complementation module, a keyword network and semantic analysis retrieval module and a semantic mapping and query response module. The system can pre-process and standardize the medical data to generate the knowledge graph containing abundant medical information, not only can complement the knowledge graph through graph structure analysis and semantic reasoning technologies to ensure the integrity and accuracy of the knowledge graph, but also can carry out efficient retrieval based on user query to improve the user experience. And the user query intention and the related entity relationship are accurately matched, and finally an accurate and comprehensive medical information query result is provided.

The invention discloses a medical whole-course intelligent management system based on a large model, and belongs to the technical field of large models. Comprising a multi-modal data acquisition module, a privacy calculation preprocessing module, a dynamic knowledge enhancement module, a time sequence data analysis module, an intelligent decision engine module, a multidisciplinary collaboration module, a patient interaction platform module, a dynamic intervention feedback module and a system security center module. The cross-mechanism data security sharing is realized, and the compliance of sensitive information processing is also ensured; a two-channel medical knowledge base is constructed, authoritative guidelines can be synchronized, newest clinical research data can be analyzed in real time, the knowledge base is kept in the newest state all the time, and the frontier scientific basis is provided for clinical decisions; dynamic modeling and trend prediction are carried out on long-term monitoring data of a patient by adopting a hybrid neural network model, and potential health risks and development trends can be identified more accurately.

The invention provides a medical science popularization article recommendation method and system based on a user portrait. According to the method, multi-source medical data are integrated, logic association of the multi-source medical data is analyzed, a dynamic knowledge graph is constructed, then in the graph, real-time diagnosis and treatment behaviors of a user and historical medical interaction records are fused to generate a user portrait label set layered according to the medical cognition level, and the understanding depth and preference of the user on medical concepts are reflected; and further migrating preference features in the portrait from a non-real scene of historical interaction to a real scene of real-time diagnosis and treatment, and generating a dynamic recommendation feature vector synchronized with medical knowledge evolution through cross-scene conversion. And finally, according to a matching result of the feature vectors and concepts in the knowledge graph, dynamically adjusting an article recommendation priority, and generating a personalized recommendation sequence fitting the real-time cognitive level of the user. According to the technical scheme provided by the invention, the accuracy and timeliness of medical science popularization article recommendation can be improved.

The invention provides a clinical decision-making method and system based on a large language model and a knowledge graph, and belongs to the technical field of artificial intelligence and intelligent diagnosis and treatment crossing. The method comprises the following steps: S1, training and deploying a created generative large language model and a knowledge extraction model; s2, extracting medical knowledge from the medical data set through a knowledge extraction model; s3, constructing a medical knowledge graph based on the medical knowledge; s4, acquiring an input medical question, inputting the medical question into the generative large language model, querying medical knowledge corresponding to the medical question by the generative large language model through the medical knowledge graph, generating a medical answer based on the medical knowledge, recording a decision basis chain in the query process, and feeding back the medical answer and the decision basis chain; and S5, recording a question and answer log including the medical questions, the medical answers and the decision basis chain. The method has the advantages that the accuracy, the reliability, the timeliness and the safety of clinical decision making are greatly improved.

The invention relates to a digital chronic disease intelligent management platform based on an AI model and multi-dimensional data fusion, clinical diagnosis and treatment data, wearable equipment monitoring data, medication record data and environment monitoring data are acquired through a data acquisition module, and after standardized preprocessing is performed through a data fusion processing module, deep analysis is performed through an AI analysis module, and the data fusion processing module performs data fusion processing; in combination with medical knowledge of the knowledge base module, the intelligent decision-making module generates a personalized management scheme, and the personalized management scheme is implemented through the intervention execution module and the intelligent interaction module. Multi-dimensional health data are processed through an AI large model, a complex mode and an association relationship are automatically learned, and accurate disease prediction and risk assessment are realized; the pertinence of the scheme and the compliance of a patient are greatly improved; and real-time interaction and personalized guidance are provided, the participation degree and the self-management ability of the patient are effectively enhanced, and a benign health management cycle is formed.

The invention discloses an inquiry model training method and device based on a long thinking chain, and relates to the field of large models, semantic information is extracted through strategy network analysis of a model, and an initial step decision is generated in combination with context information in a historical memory library; sending the initial step decision into a reasoning path generator, and reasoning to generate a primary diagnosis disease source and an intermediate diagnosis step; sending the primary diagnosis source and the intermediate diagnosis step into a verification module, performing pathological logic verification according to a case diagnosis report and a medical knowledge base, and feeding back a verification result; the reasoning path generator updates the historical memory bank based on the feedback result, the preliminary diagnosis disease source and the intermediate diagnosis steps; the strategy network continues reasoning based on user feedback input and the updated context information in the historical memory bank, and finally an inquiry result is output. According to the scheme, technical means such as reinforcement learning, self-adaptive backtracking and memory enhancement are introduced into a long thinking chain reasoning framework, so that a large language model realizes multi-aspect comprehensive improvement in medical question and answer and auxiliary diagnosis scenes.

The invention discloses a medical field large model pre-training and fine tuning method and device based on a knowledge graph, and the method comprises the steps: training a reward model through employing collected medical related text data and a quality marking result, and screening out high-quality data from the newly obtained medical related text data through employing the trained reward model; coding the medical knowledge graph into a text format processed by a large language model, and alternately inputting the high-quality data and the coded medical knowledge graph into the large language model in different batches for model pre-training; and designing an instruction fine tuning data format comprising an instruction, a knowledge graph reasoning path and a generated answer, inputting a training sample constructed in the instruction fine tuning data format into the pre-trained large language model for model fine tuning, and outputting a final answer and reasoning path according to the instruction newly input by the user by utilizing the fine-tuned large language model. According to the method, the professionality of the large language model in the medical field can be effectively improved, and AI application in high-risk fields such as medical treatment is promoted.

The invention provides a medical multi-hop text question and answer retrieval enhancement method based on heterogeneous knowledge. The method comprises the following steps: firstly, constructing a uniform heterogeneous graph structure based on a medical knowledge graph of a medical document, and establishing a semantic bridge through an entity-document mapping relationship; performing semantic decomposition on a complex medical problem input by a user by utilizing the large model, and iteratively generating a series of mutually independent atomic queries; searching a reasoning path in the entity sub-graph of the heterogeneous graph, and calculating a path score by fusing the weighted combination of the entity association text similarity, the entity matching degree and the path edge weight; training a retriever by adopting a marginal sorting loss function, and optimizing a retrieval effect through positive and negative sample comparative learning; and finally, calling a large model to convert the reasoning path with the highest score into a text, and extracting a document fragment corresponding to a path node. According to the method, the problems that an existing retrieval enhancement technology is insufficient in complex problem processing capacity, poor in reasoning interpretability and the like are effectively solved, and high-accuracy medical questions and answers are achieved.

The invention discloses a personalized medical record analysis recommendation method and system based on a patient portrait. The method comprises the following steps: S1, generating a preprocessed health data set; s2, according to the preprocessed health data set, detecting a health event of the patient through a predefined health event triggering rule and an event classification model; s3, based on the priority of the health event, setting a trigger condition to start an updating process of the patient portrait; s4, the preprocessed health data set is combined with health event information, and a dynamic patient portrait is generated through a multi-modal data fusion technology and a time sequence modeling technology; s5, calculating a similarity index and selecting an optimally matched similar medical record; s6, according to the intelligent medical record analysis result and the medical knowledge graph, a diagnosis and treatment suggestion for the current patient is generated in combination with the rule base. The intelligent level and the precision level of the personalized medical record analysis recommendation system are remarkably improved.

The invention discloses an elderly disease health question and answer method based on multiple agents and a knowledge graph, and belongs to the crossing field of artificial intelligence and medical information technologies. Structured representation of medical knowledge is realized by constructing a multi-modal medical knowledge graph and through multiple entities and association relationships thereof. In combination with a domain customized large language model and a multi-agent dynamic routing mechanism, a two-stage training strategy is adopted to optimize a base model, including all-parameter pre-training and parameter efficient fine tuning based on a low-rank decomposition technology, so that the professionality and reliability of the model are effectively improved. In a multi-agent collaborative architecture, the system dynamically selects an optimal processing path according to input characteristics, including knowledge graph query, professional answer generation or external knowledge retrieval, and maintains context continuity of multiple rounds of conversations through a dynamic abstract compression algorithm. According to the method, the multi-agent collaborative architecture is combined with the knowledge graph reasoning ability, and the advantages of relation query and semantic matching are integrated through the mixed retrieval strategy.

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 provides an intelligent medical question-answering system and method based on hybrid retrieval and lightweight reordering, and is applied to the technical field of medical data processing. According to the method, five types of core entities are extracted through the preset Chinese medical NER model, and the structured knowledge base is formed through relation extraction modeling association. Analyzing user Chinese query, extracting medical entities, identifying four types of appeals and converting the four types of appeals into semantic vectors; a PubMedBERT is adopted to encode a medical document to generate a vector for storage, BM25 and vector retrieval are executed in parallel when query is received, and candidate documents are generated through fusion of an RRF algorithm. And generating a score data set based on the prompt template, and training the lightweight model to sort and output an evidence set. In combination with query semantics, a simplified context is generated through retrieval, sorting and compression, and FlashAttention optimization calculation is integrated. An optimized U-Net segmentation image is utilized to generate a structured report, and multi-modal information is integrated to generate an accurate answer giving consideration to the image and medical knowledge through LLM reasoning.

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 method for establishing a diagnosis and treatment system for digestive system disease multi-modal information. The method comprises the following steps: S1, collecting multi-modal information for labeling and preprocessing; s2, extracting a feature vector and embedding a label into the multi-modal information according to the labeled information; s3, splicing and mapping the feature vector and the tag to a unified dimension to obtain an enhanced feature vector; s4, fusing the enhanced feature vectors to form a multi-modal feature matrix, performing linear mapping and weighted aggregation on the multi-modal feature matrix to obtain global fusion vectors, and collecting to generate a fusion vector sequence; s5, enhancing the time sequence information of the global fusion vector sequence, enhancing the spatial information of the spatial relevance of the specific feature of the part, and performing interactive fusion to obtain a spatio-temporal joint feature; s6, performing classification prediction on the disease stage or the specific pathological type, and outputting a diagnosis result; and S7, performing semantic association on the diagnosis result and the medical knowledge graph, sharing data to an online health intelligent platform, and providing a personalized decision basis for clinicians.

The invention discloses a medical image processing method and system based on artificial intelligence, and the method comprises the steps: carrying out the voice input of focus information through a voice input terminal, carrying out the preprocessing and character conversion output based on the inputted voice information, and dynamically generating a keyword set highly related to a current diagnosis task by combining structured priori knowledge of a medical knowledge graph and medical image-text multi-modal features, then generating a candidate diagnosis set based on the keyword set, calculating each semantic alignment score with the medical image features, generating a final diagnosis opinion through Langevin Dynamics sampling, and obtaining a final diagnosis result. And finally, the user terminal performs re-checking and enhanced correction, dynamically optimizes model parameters, and outputs a final formal diagnosis report based on an optimization correction result, thereby solving the accuracy and quality problems of traditional artificial intelligence on medical images, and realizing high-precision, high-robustness and interpretable artificial intelligence auxiliary diagnosis on the medical images.

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 provides a disease science popularization error correction method and system based on artificial intelligence. According to the method, the disease knowledge query content input by the user and the associated science popularization text data source are obtained, the semantic focus is dynamically recognized by utilizing the pre-trained language model, and the context-associated semantic focus distribution graph is generated. On the basis of cross-modal correlation analysis of a map and multi-source heterogeneous medical representation, a potential cognitive deviation region is positioned, and an error correction priority queue is constructed in combination with authority weight differences of medical knowledge nodes. Incremental correction is further carried out on the error expression according to the queue priority, and an error correction feedback result containing medical traceability information is generated while the original semantic intention of the user is reserved; according to the technical scheme provided by the invention, high-precision error correction of disease science popularization content and closed-loop feedback of medical traceability information are realized, and user cognition accuracy and knowledge credibility are improved.

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 provides a clinical research medical supervision method and system based on artificial intelligence, and relates to the technical field of medical supervision. The method comprises the following steps: constructing a medical supervision ontology knowledge base by obtaining related medical knowledge texts, and mapping medical supervision data sample data into an ontology framework based on the knowledge base to form a medical supervision knowledge graph. Then, knowledge extraction is carried out on the medical supervision application data to obtain review elements, the review elements are mapped into a knowledge graph to obtain related ontology concept nodes, and a review element graph is formed; then, reasoning and judging the review element graph based on the medical supervision knowledge graph and a preset rule to form a preliminary review result; and finally, performing quantitative scoring on the preliminary review result, and performing review decision based on the score and the preliminary result to obtain a final review decision result. According to the method, the medical inspection efficiency and the inspection quality are improved.