2675 results about "Medical information" patented technology

The invention discloses a data analysis platform management method and system, and belongs to the technical field of computer data processing, and the method comprises the steps: obtaining multi-source heterogeneous data from a medical information system; establishing a cross-system data association model, performing dynamic feature extraction and weight distribution on multi-source heterogeneous data, and outputting a resource supply-demand relationship graph; analyzing the resource supply-demand relation graph, and identifying business conflicts in medical resource scheduling; generating a conflict solution of the business conflict based on a multi-objective optimization algorithm, and generating an execution instruction; and issuing an execution instruction to the target medical system, verifying an execution result of the execution instruction, and updating system parameters. According to the method, a cross-system dynamic modeling and multi-objective optimization strategy is adopted, and a closed-loop verification mechanism and a multi-source heterogeneous data fusion technology are combined, so that intelligent prediction, accurate scheduling and self-learning optimization of medical resource conflicts can be realized, full-process traceable execution of high-value consumables is ensured, and the resource utilization efficiency and the emergency response capability are comprehensively improved.

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 relates to the technical field of medical artificial intelligence and knowledge graph construction, in particular to a rare disease knowledge graph construction method based on modal injection and multi-modal fusion. Comprising the following steps: S1, collecting multi-modal medical information including texts, images and genes; s2, standardization processing is carried out, and a three-layer metadata structure is constructed; s3, complementing missing modal data, and performing feature extraction and unified dimension conversion on the modal data to realize representation alignment in a shared semantic space; s4, performing multi-level semantic fusion to obtain a unified fusion semantic vector; and S5, constructing a double-layer structure system rare disease knowledge graph comprising an ontology layer and an instance layer. According to the method, multi-modal medical information of texts, images and genes is selected to construct the knowledge graph of the rare disease, the application range, coverage and accuracy of the knowledge graph are improved, correspondence adaptation of rare cases during clinical diagnosis and treatment of the rare disease can be achieved, and the method has high recognition capacity.

The invention discloses a nursing risk intervention decision-making system and method based on a knowledge graph, and relates to the technical field of medical informationization, and the method comprises the steps: building a dynamic medical nursing knowledge graph based on multi-source nursing data and a deep learning model, and carrying out the entity recognition through a bidirectional long-short term memory network and a conditional random field; capturing evolution characteristics of an entity relationship through a marginal attention mechanism and a time sequence diagram convolutional network, and optimizing a knowledge graph structure based on connectivity constraints and a relationship weight threshold; a knowledge structure is dynamically adjusted by using a strengthened learning driven graph optimization mechanism, the relationship weight between entities is optimized in real time, a multi-level risk representation model is constructed, and accurate recognition and early warning of nursing risks are realized through local risk feature extraction, global risk information aggregation and time sequence risk evolution analysis. According to the method, the complex association relationship between the medical entities and the nursing risk decision are deeply fused, and the multi-dimensional medical entity features are constructed into the association network between the entities, so that early risk identification is realized.

The invention discloses a real-time health risk prediction method and system based on a dynamic knowledge graph, and relates to the technical field of medical information. The method comprises the following steps: carrying out multi-modal fusion and privacy protection preprocessing on medical and nursing heterogeneous data, and realizing semantic consistency of cross-mechanism data based on an entity alignment method of a cross-modal graph neural network; based on a hierarchical federated learning framework, local model parameters are subjected to hierarchical encryption and aggregation through a secure multi-party computing protocol to generate an initial global model, and prediction distribution of the global model is optimized in combination with knowledge distillation of differential privacy constraints; designing a gradient difference dynamic updating trigger mechanism of noise robustness, smoothing noise interference through a sliding window mean value, and realizing adaptive threshold calibration through linkage model performance verification; and light-weight deployment real-time reasoning is realized based on redundant edge pruning of confidence and 8-bit symmetric quantization. On the premise of protecting data privacy, the real-time performance and accuracy of health risk prediction are remarkably improved, and the method is suitable for a cross-institution medical care collaborative decision-making scene.

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 the field of medical informatization. The invention discloses a thyroid cancer electronic medical record system based on multi-modal data fusion. The thyroid cancer electronic medical record system comprises a multi-modal data acquisition module which acquires patient texts, ultrasonic images, genes, biochemical indexes and clinical data and performs standardized calibration to generate standard data; the multi-modal feature extraction module extracts semantic, structure, mutation, change and fluctuation features of each standard data through multiple technologies; the single-mode prediction model construction module constructs single-mode prediction models of texts, images and the like based on the features and outputs results; and the multi-modal fusion prediction module fuses the single-modal model based on the deep learning framework to output a multi-modal fusion prediction result. According to the invention, multi-modal data are integrated, and the accuracy and comprehensiveness of thyroid cancer diagnosis are improved. The system ensures consistency through standardized data processing, and assists doctors to accurately judge pathological types, recommend therapeutic schedules and evaluate prognosis by means of a multi-modal feature extraction and fusion mechanism.

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 relates to a medical information processing and intelligent data management technology, and discloses a hospital multi-source heterogeneous data integration management system and method based on an EMPI, and the system carries out the real-time subscription and batch extraction of the data of an HIS, an EMR, an LIS, a PACS, a DRG and a pre-hospital first-aid system through an interface adapter, and generates a unified main index identifier through the matching of the EMPI, and carries out the real-time subscription and batch extraction of the data of the HIS, the EMR, the LIS, the PACS, the DRG and the pre-hospital first-aid system. Field-level cleaning, standardization and semantic mapping are carried out on the free text and the structured data, an intermediate standardized record set is formed, an event graph is constructed, event cluster recognition is carried out in combination with time, clinical entities, doctor seeing scenes and department similarity, consistency judgment and EM iterative fusion are carried out on the basis of data source reliability, and event cluster recognition is carried out. And outputting a unified patient file set and a downstream service interface. According to the invention, high-precision integration, standardized management and traceable application of multi-source data are realized, and a reliable data basis is provided for clinical decision support, medical insurance management and scientific research.

The invention provides a gynecological disease diagnosis and treatment method and system fused with an electronic medical record, and relates to the technical field of medical informationization, and the method comprises the steps: collecting the multi-modal diagnosis and treatment data of a patient, and constructing a personalized medical database through a self-encoding network; deploying a plurality of agents based on a dynamic causal network, and generating an initial treatment scheme in combination with deep reinforcement learning; through real-time data acquisition and a multi-objective optimization algorithm, the scheme is dynamically optimized by fusing expert decision features. According to the invention, personalized precise treatment of gynecological disease diagnosis and treatment can be realized, the treatment effect is improved, and the complication risk is reduced.

The invention discloses a medical information multi-agent expert thinking chain collaborative reasoning method and system, and the method comprises the steps: constructing a sub-expert agent and a summarizer agent based on a large language model, processing the medical information, and outputting a processing conclusion of the medical information; and secondly, constructing an evaluator agent, performing medical information conflict evaluation on the output of the sub-expert agent and the summarizer agent, activating a multi-agent debate mechanism when the conclusion output of the summarizer agent exceeds a medical conflict threshold, and outputting whether a processing conclusion is credible or not. Then constructing a coordinator agent, selecting called sub-expert agents and summarizer agents, and generating a plurality of forward reasoning thinking chains; and finally, the evaluator agent evaluates whether the output of the forward reasoning thinking chain exceeds a medical conflict threshold, and iteratively updates an output processing conclusion. According to the method, complex problems in medical information processing problems can be analyzed, and reasoning accuracy and interpretability are remarkably improved.

The invention discloses an electronic medical record automatic quality control system and method based on a large language model, and the method comprises the steps: extracting medical record data from a medical information system, and carrying out the data preprocessing; and performing deep semantic analysis on the medical text by utilizing a large language model, and extracting key diagnosis and operation information. And performing semantic representation on the medical terms by fine tuning the pre-training language model and combining with a multi-layer attention mechanism. And constructing an ICD coding knowledge graph, and calculating the semantic similarity between the diagnosis terms and the ICD coding terms in the electronic medical record by using an embedded vector technology to realize automatic matching. And according to a predefined quality control rule, automatically checking the electronic medical record through the logic expression, and generating a quality control report and an improvement suggestion. And finally, visually displaying quality control and coding results, and outputting a quality control report. The electronic medical record quality control efficiency and accuracy are improved, and standardization and automation of quality control work are achieved.

The invention discloses a blood tumor patient clinical data analysis method and system based on a knowledge graph, and relates to the technical field of medical information processing. The method comprises the following steps: analyzing an unstructured text by utilizing a natural language processing model, and constructing a standardized entity set; aggregating laboratory indexes through a dynamic window, and constructing a time axis data set for marking a treatment stage; a standardized entity is integrated based on a blood tumor ontology model, a dynamic global knowledge graph is constructed in combination with a clinical causal relationship and a time sequence path, and chain reaction early warning is realized; based on the real-time patient sub-atlas, successful cases with the same molecular features are retrieved in the knowledge atlas to generate a candidate treatment scheme set, a graph neural network trained through an optimization algorithm is utilized to predict the complication risk of each scheme, and finally the optimal treatment scheme is evaluated and selected; according to the method, a clinical data barrier is broken through, dynamic reasoning, advanced risk early warning and personalized scheme optimization of the whole treatment cycle are realized, and the diagnosis and treatment accuracy, safety and medical resource utilization efficiency are remarkably improved.

The invention relates to the field of medical informatization, in particular to a nursing risk assessment system and method based on multi-modal data. According to the nursing risk assessment system, data acquisition, preprocessing, multi-modal fusion, risk assessment and early warning are integrated, and comprehensive, accurate, timely and personalized nursing assessment is realized. The system collects physiological signals, nursing behaviors and environment data, the data are preprocessed and fused to generate feature vectors, a risk assessment matrix is constructed, and risk indexes are calculated. The risk early warning module sends early warning information to the nurse work station in real time, and nursing safety is ensured. Innovative technologies such as multi-modal data fusion, real-time dynamic evaluation, personalized adjustment and the like greatly improve the nursing quality, reduce the occurrence rate of adverse events, relieve the burden of medical staff and improve the utilization efficiency of medical resources. Long-term application is hopeful to significantly improve patient prognosis and reduce medical cost, and makes an important contribution to development of intelligent medical treatment and precise nursing.

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 discloses a knowledge graph-based nursing corpus automatic creation method, and relates to the field of artificial intelligence technology and medical information processing, and the method comprises a multi-dimensional data acquisition stage, a knowledge graph construction stage, a corpus automatic generation and updating stage, and a corpus quality control and evaluation stage. According to the automatic corpus generation method driven by the knowledge graph, dynamic generation and real-time updating of corpora are achieved by constructing an ontology structure, a corpus generation template and an updating algorithm in the nursing field; through a deep learning model and a transfer learning technology, new nursing knowledge and a diversified information structure are continuously introduced into the corpus, and the diversity of corpus content is increased.

The invention relates to the technical field of medical information, in particular to an intelligent prediction model and method for postoperative complications of anesthetized patients, and the method comprises the steps: collecting preoperative to postoperative complete-cycle clinical data of a patient through a medical data interface; analyzing operation codes to generate risk features, extracting vital sign dynamic features, and establishing a complication probability mapping relation through a multi-modal fusion network; combining the complication probability and pharmacokinetic parameters to construct an optimization model, and solving an individualized anesthetic dosage interval by using a gradient descent algorithm; vital signs are dynamically monitored in the operation, a dose re-optimization mechanism is triggered, the infusion rate is adjusted, and a closed-loop control link is formed; and generating a visual decision report. According to the method, through deep integration of complete-cycle clinical data and multi-modal feature modeling, preoperative physiological parameters, operation coding semantic information and intraoperative vital sign dynamic modes are subjected to fusion analysis, a nonlinear mapping relation between dosage and complication probability is constructed, and the risk prediction precision and individualized adaptability are remarkably 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 relates to the technical field of medical information processing, in particular to a health data processing method and system based on a distributed ledger library, and the method comprises the steps: a node firstly links a health data abstract to obtain a differential privacy budget token and a reversible tensor hash key; then, carrying out encryption training on local model parameters by using the key, injecting Gaussian noise according to budget to generate a differential protection gradient, and carrying out uplink together with zero-knowledge proof; the account book end decrypts the threshold value, uses a structural equation model to deduce a causal correction matrix according to the reputation weight aggregation gradient, and automatically adds a budget and turns the key when the concept drifts and the budget is insufficient; the operation mechanism splices the aggregation parameters and local parameters, generates a gating vector in combination with a causal correction matrix and a reputation weight, outputs disease risk prediction, and only uploads prediction hash and error information; chain-level audible privacy protection, dynamic budget management and hybrid deviation suppression are realized, and the safety and accuracy of a cross-institution medical model are improved.

The invention relates to the technical field of medical rehabilitation, and discloses a lower limb weight-bearing gait rehabilitation training system which comprises a data acquisition module, a data processing and analysis module, a patient individualized modeling module, an intelligent decision and control module, a rehabilitation execution module and a man-machine interaction and medical information interface module which are in communication connection through a network. The data acquisition module is used for acquiring multi-modal data of a patient in real time, and the multi-modal data comprises static sign data, dynamic physiological parameters, kinematics and dynamics parameters and non-motion physiological and psychological state data; and the data processing and analysis module is used for carrying out preprocessing, feature extraction and deep analysis on the original data, and outputting a structured patient individualized feature vector and an evaluation result. According to the invention, a patient three-dimensional skeletal muscle digital twinborn model is constructed through the patient individualized modeling module, and in combination with a continuous learning intelligent model library, body sign differences of different patients can be accurately adapted.

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 information processing, in particular to a main diagnosis rationality judgment method and system based on artificial intelligence. The method comprises the following steps: acquiring diagnosis request data, and performing candidate main diagnosis generation on the diagnosis request data by using preset medical knowledge graph data and preset intelligent detection rule data to obtain candidate main diagnosis data; performing diagnosis execution parameter generation according to the candidate main diagnosis data to obtain candidate main diagnosis parameter data; acquiring historical diagnosis request data and historical candidate main diagnosis data, and performing diagnosis linkage processing according to the historical diagnosis request data and the historical candidate main diagnosis data to obtain diagnosis linkage network data; and performing node interaction processing on the candidate main diagnosis data according to the diagnosis linkage network data to obtain candidate main diagnosis weight data. According to the method, the intelligent level of diagnosis rationality judgment, the credibility of a scoring result and the adaptability of a clinical system are improved.

The invention relates to the technical field of medical information, in particular to an ophthalmology information extraction system based on electronic medical records. The system comprises an electronic medical record data acquisition and cleaning module, a domain knowledge driven ophthalmology information extraction module, a multi-modal data fusion and labeling module and a structured result generation and display module, and original medical record data can be acquired; performing data cleaning on the original case data to generate a patient electronic case data set; therefore, through the whole-process modular design and multi-modal data fusion processing, the problems that in a traditional ophthalmic case information extraction mode, data processing is not coherent, information extraction is not accurate, and results are difficult to directly express are solved, and the data utilization rate and clinical application value are improved.

The invention discloses a chronic disease follow-up visit and management platform driven by a large model, and relates to the technical field of medical information technology platforms. The system is applied to managing and tracking the follow-up visit process of chronic disease patients and providing health consultation and answers, and comprises a user interface module, a logic control module, a database module, a large model module and an auxiliary function module, and the user interface module is used for providing an interactive interface between a patient and a doctor. An intelligent and efficient system platform is constructed by integrating a large model technology, a voice recognition technology and a digital human technology, the system platform adopts a privatized large model, data security and privacy protection are ensured, medical institutions are allowed to autonomously optimize and control the application of the model, and on the whole, through an advanced technical means, the intelligent and efficient system platform has the advantages of being high in practicability and easy to popularize. Refining, individuation and high efficiency of chronic disease management are remarkably improved, and the purpose is to improve the health outcome and life quality of patients.

The invention relates to the field of medical informatics, in particular to an anesthetic dosage optimization method based on artificial intelligence. Comprising the following steps: collecting real-time physiological data and static data of a patient, converting the real-time physiological data and static data of the patient into standardized feature vectors through standardization processing, and predicting the initial infusion rate of an intraoperative anesthetic by using a long-short-term memory network; collecting and standardizing the intraoperative data of the patient to obtain the standardized intraoperative data; a personalized anesthetic infusion rate is generated through a dynamic metabolism risk and nerve sensitivity joint optimization algorithm; the maximum allowable infusion rate and the minimum effective infusion rate are determined based on a clinical guide, the remaining time of an operation is obtained, safety verification is carried out, and the final infusion rate is generated in combination with the remaining time of the operation. The technical problem that the prediction of the initial infusion rate of the preoperative anesthetic is inaccurate due to the fact that the initial infusion rate is determined by depending on the experience of an anesthetist in the prior art is solved.

The invention is suitable for the technical field of tumor treatment and protection, and particularly relates to an intelligent classification prediction method, system and equipment for tumor treatment and protection, and the method comprises the steps: obtaining tumor-related molecular marker detection values and clinical diagnosis and treatment features of all target patients, and comprehensively integrating the multi-dimensional medical information of the patients; according to the relevance of molecular markers and clinical features, a scientific and quantitative multi-dimensional prognosis layering index set is constructed, and an accurate layering evaluation framework is formed; inputting the medical data into a pre-trained multilayer neural network model, and generating a high-resolution tumor characterization map through a cross-modal feature fusion technology; based on the dynamic matching of the atlas and the hierarchical index set, the hierarchical contribution degree parameter and the prognosis risk score are calculated in combination with the random forest model, the precise quantitative evaluation of the condition of the patient is completed, a high-credibility quantitative basis is provided for clinical decision making, the over-treatment risk is effectively reduced, and the prognosis management effect of the patient is improved.

The invention discloses a special disease queue data capturing method and system based on an intelligent medical knowledge graph, and relates to the technical field of medical information, and the method comprises the following steps: S1, constructing a special disease intelligent medical knowledge graph which comprises a bidirectional mapping relation between standard terms of a single disease category and clinical actual corpora, clinical text data is accumulated in a mode of combining manual annotation and machine learning, and a domain exclusive knowledge base containing symptoms, diagnosis and examination indexes is formed. According to the special disease queue data capturing method and system provided by the invention, by constructing the special disease intelligent medical knowledge graph, bidirectional mapping of single disease specification terms and clinical actual corpora is realized, and the problem of insufficient semantic understanding when non-standardized clinical corpora are processed by a traditional method is effectively solved; the entity information in the unstructured medical data can be accurately extracted by utilizing a natural language processing model and an inference engine.

The invention discloses a plastic effect simulation display method and system based on a 3D simulation technology, and relates to the technical field of medical information, and the method comprises the steps: collecting the facial data of a patient, and constructing a multi-level facial structure initial model; constructing a hybrid deformation model; individual characteristic parameters of a patient are extracted, and an individual biomechanical parameter set is generated through the biomechanical parameter prediction network; applying the personalized biomechanical parameter set to the mixed deformation model; operation input parameters of plastic surgery are received, and the deformation result of each layer of structure is calculated and displayed in real time based on a GPU parallel computing framework; constructing a tissue healing and recovery kinetic model, and generating appearance prediction data at different postoperative time points in combination with the deformation result; and performing three-dimensional visualization processing on the appearance prediction data. According to the method, CT / MRI, 3D structured light scanning and expression dynamic video sequences are creatively integrated, the limitation of traditional single-mode data modeling is broken through, and the soft tissue boundary recognition precision is remarkably improved.

The invention discloses an intelligent construction and retrieval method for a medical knowledge graph based on GraphRAG and LLM. The intelligent construction method of the medical knowledge graph comprises the steps of data collection and processing, text segmentation, entity and relation extraction, entity analysis, weak connected component identification, word distance filtering, LLM evaluation, element abstract generation, graph community generation and community abstract generation. The intelligent retrieval method comprises the steps of knowledge graph vector library construction, text vector library construction, user question entity extraction and vectorization processing, multi-layer entity and relation retrieval, text retrieval, LLM summarization, result output and user feedback collection. According to the method, the GraphRAG and the LLM large model technology are combined, and efficient retrieval and accurate presentation of the medical information are achieved. According to the system, the accuracy and efficiency of medical literature retrieval are remarkably improved through deep integration of graph structured data and natural language processing capacity.