4305results about "Medical communication" patented technology

A federated distributed computational system enables secure oncological therapy optimization through robotic integration. The system establishes a distributed graph architecture with secure communication channels connecting computational nodes, implementing encryption protocols for cross-institutional data exchange. Each node contains processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration while maintaining hierarchical knowledge graphs of oncological biomarkers, interventions, and outcomes. The system coordinates domain-specific knowledge through token-space communication and implements an advanced robotic integration system for surgical interventions using spatiotemporal tumor mapping, multi-modal fluorescence imaging, surgical robot coordination, and space-time stabilized mesh management. Key capabilities include wavelength-specific multi-modal fluorescence detection, combined epistemic and aleatoric uncertainty estimation, tensor-based data integration with adaptive dimensionality control, and light cone search for adaptive treatment optimization—all while maintaining strict privacy controls.

An apparatus and method for real-time assessment and mapping of quality of life indicators includes: selecting a set of quality of life dimensions, receiving objective quality of life indicators based on the quality of life dimensions, receiving subjective quality of life indicators based on the quality of life dimensions, performing a statistical analysis on the objective quality of life indicators and the subjective quality of life indicators, storing the objective quality of life indicators, the subjective quality of life indicators, and the results of the statistical analysis in a database, and outputting the objective quality of life indicators, the subjective quality of life indicators, and the results of the statistical analysis.

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 provides a psychological crisis multi-stage joint control method and system based on a psychological large model, and aims to realize real-time monitoring, accurate evaluation and intelligent intervention of psychological states through a multi-modal data fusion and deep learning technology. The system collects multi-source information such as texts, voices, videos, physiological signals and behavior data, performs cross-modal analysis by using models such as Transform, LSTM and CNN, constructs personalized psychological portraits, and analyzes and predicts the psychological state change trend in combination with a time sequence. According to the method, a psychological crisis dynamic grading model is adopted, the psychological state of a user is divided into a normal grade, a mild grade, a moderate grade and a severe grade, multi-grade intelligent intervention is provided based on different risk grades, and the multi-grade intelligent intervention comprises AI self-service adjustment, psychological counseling matching, social support enhancement, emergency medical intervention and the like. The psychological intervention strategy is optimized in combination with reinforcement learning, the intervention mode is dynamically adjusted according to user feedback, and individuation and adaptability are improved.

The invention provides a multifunctional health monitoring and early warning system based on radar and vision fusion, and relates to the technical field of intelligent early warning, and the system comprises a feedback receiving module which receives a radar feedback signal and an image feedback signal; the monitoring identification module traverses the target monitoring area to determine monitoring data, and carries out monitoring identification; the abnormity judgment module is used for carrying out fusion analysis on the first monitoring data set and the second monitoring data set, carrying out bidirectional abnormity judgment and generating an early warning instruction; and the risk analysis module uploads the abnormal judgment result to a cloud platform for risk analysis, and sends early warning information to a remote terminal according to the risk level. According to the invention, the technical problem that the accuracy of early warning information is affected due to high false alarm rate caused by environmental infection due to dependence on a single sensor in health monitoring in the prior art can be solved, risk assessment is carried out by combining radar and visual fusion, the accuracy of anomaly recognition is improved, and the accuracy of early warning information is improved. And the early warning real-time performance and accuracy are 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 psychological health monitoring, in particular to a self-injury behavior early warning and intervention method and system based on multi-modal physiological data and AI.The method comprises the steps that physiological signals, behavior data, text data and environment parameters of a user are collected in real time based on a contact sensor and non-contact sensing equipment, and the physiological signals, the behavior data, the text data and the environment parameters of the user are obtained; generating a multi-source original data set; performing time synchronization and spatial correlation analysis on the multi-source original data set to form a fusion feature vector; inputting the fusion feature vector into a preset multi-modal deep learning model, obtaining a self-injury behavior risk score value, and dividing risk levels based on the self-injury behavior risk score value; and triggering a hierarchical intervention strategy of AI psychological counseling, emergency contact notification and medical resource linkage according to the risk level. The method has the effect of improving the precision and quality of teenager mental health assessment and self-injury behavior early warning results.

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 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 discloses a doctor-patient negotiation method and device fusing LLM and a multi-agent architecture, equipment and a medium, and relates to the technical field of doctor-patient negotiation. The doctor-patient negotiation method comprises the following steps: acquiring doctor-patient background information, and constructing a doctor agent, a patient agent and a coordination agent based on a large language model. The doctor agent generates a preliminary treatment regimen. The patient agent generates feedback to the preliminary treatment regimen. The coordination agent parses the feedback, generating a structured acceptance score and a semantic vector. The dynamic belief is updated based on the semantic vector doctor agent. And performing fuzzy utility evaluation on the current treatment scheme, and judging whether strategy adjustment is triggered or not. And the coordination agent calculates the adjustment amount of each issue based on the acceptance difference value, the patient yielding willingness coefficient and the maximum yielding amplitude, and obtains a new quotation scheme. And triggering a belief calibration and strategy reconstruction process at a preset stage node of multiple rounds of negotiation to obtain a new strategy. And when the negotiation round reaches a preset maximum round number, outputting a final treatment scheme.

A device and associated computer-implemented method for obtaining a trained model for prediction of at least one risk score of a user for developing an abnormality with respect to a health status of the user, and to a device and associated method for assisting the user by providing at least one health-related instruction for improving a health status of the user, comprising using a trained model for prediction obtained by the device.

The invention relates to the technical field of medical monitoring, in particular to a multi-mode intelligent management system for monitoring and preventing stress injury. The system comprises a multi-modal data acquisition and preprocessing module, a deep fusion risk prediction model module, an intelligent path planning and resource allocation module, a personalized intelligent intervention module and a data security module, so as to acquire time sequence physiological data and generate a body pressure thermodynamic diagram in real time, and realize standardization through normalization; a risk prediction model is generated through modeling, the contribution degree of each modal feature is calculated, and a risk level is determined according to a preset early warning grading mechanism; an optimal path is generated through intelligent planning, a nursing scheme is dynamically optimized and generated according to the risk level, and the nursing scheme is encrypted and transmitted to a cloud platform through HTTPS to be regularly subjected to security vulnerability scanning and repairing. According to the invention, the skin condition, the body position change and the pressure distribution of the patient can be accurately monitored, so that the closed-loop management of the pressure injury is realized.

The invention relates to the technical field of intelligent decision making, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a multi-modal data-based event analysis method, which comprises the following steps of: obtaining attribute data, monitoring data, environment data and biological characteristic data of a user, and performing standardization processing on the attribute data, the monitoring data, the environment data and the biological characteristic data; combining semantic analysis and structured fusion of the text records to form a unified input data set; and performing multi-modal fusion modeling on the unified input data set by using an intelligent decision model, generating a potential event analysis result of an individual state, generating a visual report containing potential event levels, main influence factors and intervention suggestions based on the result, and finally sending the report to a user terminal. According to the method, the multi-source structured data and the multi-source unstructured data are integrated, and the intelligent decision-making model is introduced to realize feature level fusion and semantic enhancement modeling, so that the accuracy and timeliness of individual state analysis are effectively improved, and the personalized expression of an analysis result is enhanced.

The invention discloses a remote monitoring and guiding system for rehabilitation training in the orthopedics department, particularly relates to the technical field of medical rehabilitation intelligent monitoring, and is used for solving the problems that in an existing remote rehabilitation system, timing sequence dislocation exists in action recognition and regulation and control instruction execution, and joint movement safety early warning is delayed. According to the method, localized time sequence alignment processing is performed on joint movement data and electromyographic signals based on edge computing nodes, a joint linkage action time sequence difference is generated, and an abnormal action risk level is judged in real time by combining an electromyographic signal pre-activation feature and a conflict detection mechanism of a standard action intention; historical abnormal records are matched through the cloud platform to generate a hierarchical regulation and control instruction set, and progressive safety intervention is triggered by the patient end equipment according to the dynamic deviation of the joint activity; through a collaborative mechanism of edge side action stage accurate analysis, electromyographic signal feed-forward verification and multi-stage instruction dynamic binding, millisecond-level response of joint linkage abnormity in staged rehabilitation training is realized, and the risk of joint over-limit activity is effectively avoided.

Systems and methods are disclosed for preserving patient privacy while allowing health data to be analyzed, managed, and stored in different geographical areas. One method for managing cross-border health data while preserving patient privacy includes: receiving a DICOM object from a hospital computing device for analysis; generating a unique case identifier for the DICOM object; validating the received DICOM object; if, based on the validation, the received DICOM object is valid, anonymizing the received DICOM object; updating the anonymous DICOM object to include the unique case identifier; compressing the updated DICOM object; and sending the compressed DICOM object to at least one data analysis web service(s).

The invention discloses a critical patient data index monitoring system and method, and the system comprises a data collection and fusion module which is used for accessing a multi-source medical device, and carrying out the data collection, protocol analysis, time sequence alignment and noise filtering; the dynamic association analysis module is used for constructing a risk conduction relation chain based on a knowledge graph and a dynamic association entropy weight algorithm and discovering a novel association relation in real time; the intelligent decision module is used for risk prediction, early warning generation and decision support; the interactive application module is used for displaying a risk conduction path and pushing early warning information; the dynamic association analysis module further comprises a knowledge graph library used for storing a pathological relation chain; the association chain generator is used for automatically generating a potential risk conduction path on the basis of the knowledge graph; the risk simulation engine is used for carrying out large-scale pathological state deduction and carrying out efficient calculation based on a GPU acceleration Bayesian network; the method has the characteristics of accuracy and comprehensiveness.

The invention discloses an oral diagnosis and treatment patient service platform based on reinforcement learning, and relates to the technical field of diagnosis and treatment services, and the platform comprises a health scoring module which integrates multi-source data, generates personalized oral health scores through a dynamic weight distribution model, and displays high-risk items in combination with 3D visualization; the patient management and marketing module tracks patient behaviors, clusters and classifies the patient behaviors, generates a dynamic follow-up visit path by using multi-target reinforcement learning, and matches a precise marketing verbal skill through a knowledge graph; the in-diagnosis auxiliary module synchronizes health data of the patient to the doctor terminal in real time, detects prescription conflicts and performs early warning, and generates personalized communication verbal skills in combination with historical preferences of the patient and real-time emotion analysis; through multi-dimensional data integration and intelligent processing, the whole oral diagnosis and treatment process is optimized, the diagnosis and treatment efficiency and the patient satisfaction degree are improved, the operation cost is reduced, and remarkable innovativeness and practicability are achieved.

The invention relates to the technical field of natural language processing, and provides a psychological medical map retrieval method based on an enhanced large model, which comprises the following steps of: aiming at a user input problem, extracting a subject entity by adopting a large language model; obtaining a user intention label by adopting a light-weight large model; based on a user intention label, controlling a relationship screening and path expansion process of the knowledge graph, and constructing a semantic path set; and judging whether the semantic path set has enough semantic support to answer the user question, and if so, outputting an answer according to the question input by the user and the semantic path set. On the basis of an existing reasoning framework, a fine-tuning lightweight large language model is introduced to perform multi-level semantic intention recognition on user questions, and a relation path selection process in a knowledge graph is controlled accordingly, so that a mechanism of scoring and sequencing paths hop by hop depending on the large language model in an original framework is replaced; therefore, the user can obtain scientific and complete psychological health knowledge, and the reliability of psychological health service is improved.

The invention discloses a cardiovascular medicine disease early warning analysis system based on big data, and relates to the technical field of disease early warning, and the system comprises a data processing and modeling module which is used for collecting physiological signal data and home behavior data of a patient, and constructing a physiological baseline model representing the personalized health state of the patient; the signal decoupling and correcting module is used for calculating to obtain corrected physiological indexes without the influence of behavior disturbance based on the home behavior data collected in real time; the risk quantitative evaluation module is used for generating a composite risk score representing the current cardiac function deterioration degree based on the deviation degree of the corrected physiological indexes and the physiological baseline model; and the early warning and adaptive optimization module is used for carrying out risk level judgment and triggering corresponding early warning. According to the method, behaviors and physiological signals are decoupled, personalized baselines are constructed, multi-dimensional risks are quantified, closed-loop optimization is formed, the problem of high false alarm and missing alarm in traditional early warning is solved, and accurate early warning and long-term effective monitoring are achieved.

The invention discloses an AI nursing whiteboard system and method based on a large model agent, the system comprises a nursing whiteboard terminal, an AI intelligent chain platform and a database, the nursing whiteboard terminal comprises an AI nursing assistant, and a whiteboard control system is arranged in the nursing whiteboard terminal. Through the functions of integration of multi-source data, intelligent early warning, accurate question and answer and the like, various diagnosis and treatment data, such as vital signs, medical advices, nursing records and the like, of a patient can be rapidly integrated and presented in a clear and visual mode, a large amount of time and energy are saved, the patient can rapidly know the condition of the patient, and the diagnosis and treatment efficiency is improved. Vital signs and illness state changes of a patient can be monitored in real time, early warning can be immediately given out once abnormal conditions such as heart rate and blood pressure exceeding normal ranges occur, a nurse can know the abnormal conditions in the first time and take corresponding measures, the response speed to emergency situations of the patient is greatly increased, and when the nurse encounters a question in the aspect of medical knowledge, the nurse can timely take corresponding measures. Inquiry may be made by an AI care assistant.

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.

A digital healthcare architecture including a cloud-based virtual clinic platform configured to facilitate remote therapy of one or more patients based on biometric authentication systems and methods. In an example arrangement, one or more biometric indicia of a user (e.g., a clinician and / or a patient) as well as one or more non-biometric input factors may be used in authenticating the user prior to granting access to a protected resource or application (e.g., a therapy application executing on a UE device) configured for effectuating remote programming of an implantable medical device (IMD) operative to provide therapy to the patient.

The invention discloses an intelligent system and method for fusing mental health evaluation in an interest scene based on an education robot. The system realizes non-intrusive mental health assessment by constructing a technical architecture of'multi-modal biological feature anonymization-quantum hybrid encryption-federated learning privacy protection-dynamic risk assessment '. The method is characterized by comprising the following steps of: (1) generating irreversible biological characteristic codes by adopting a chaos random number generator, and realizing anonymized association by combining a double hash chain technology; (2) designing a quantum enhanced hybrid encryption system, fusing an SM4 algorithm, NTRU post quantum cryptography and quantum key distribution; (3) developing a dynamic scene generator, and naturally fusing psychological test questions into interest topics by using a generative adversarial network (GAN) and a curiosity engine; (4) constructing a multi-modal emotion analysis system, and combining an LSTM + CNN hybrid model to realize physiology-behavior-voice data fusion analysis; and (5) deploying a federated learning framework, and protecting model training data through differential privacy noise injection (epsilon = 0.5).

The invention provides a non-invasive electronic medical record generation method and system, and relates to the technical field of medical informatization and artificial intelligence. The method comprises the following steps: acquiring a doctor-patient dialogue audio, and extracting structured medical record information based on a natural language processing model after speaker role distinguishing and voice-to-text conversion; comparing the information with a medical record standard to check the integrity, and generating a prompt to guide a doctor to supplement inquiry when the information is lost; finally, information is non-intrusively filled in in an analog input manner by identifying a graphical user interface of an existing medical information system. The method solves the problems of low medical record input efficiency and difficulty in integration and transformation in the prior art, and has the beneficial effects of non-invasive deployment, low cost, high efficiency, capability of ensuring medical record quality and the like.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an operation guidance and optimization method, device, equipment and medium, and the method comprises the steps: obtaining multi-source heterogeneous data to construct a dynamic knowledge graph, and generating an initial guidance scheme containing target operation, and in combination with the visual interaction model, presenting a target operation in real time, collecting real-time dynamic data, generating operation correction information based on data differences, updating display contents of the visual interaction model, and dynamically optimizing a guidance scheme according to an effect analysis value. According to the method, the dynamic knowledge graph is constructed by fusing multi-source heterogeneous data, real-time presentation and data acquisition are performed in combination with a visual interaction model, operation correction information is generated based on data differences, and a guidance scheme is dynamically optimized, so that personalized, dynamically updated and real-time feedback guidance scheme generation and optimization in multi-field application are realized; and the operation accuracy and adaptability are improved.

The invention discloses a multidisciplinary combined diagnosis and treatment method and system, relates to the technical field of intelligent medical systems, and solves the problems that traditional manual consultation is low in efficiency and is greatly interfered by human factors, simple system recommendation is inaccurate, and diagnosis and treatment requirements of patients are difficult to meet. The method comprises the steps of generating a consultation scheme based on a preset matching degree standard, an illness condition and a diagnosis and treatment specification, triggering a payment process after system compliance verification, and locking consultation resources after payment succeeds; according to the illness state emergency degree of the patient, expert scheduling information and hospital resource occupation condition, intelligent scheduling is carried out to generate a consultation time arrangement, and meanwhile, consultation invitation and detailed information are sent to related experts; and after the consultation is finished, the system arranges text records, fuses expert opinions, generates a structured consultation report and pushes the structured consultation report to a patient end. The method has the advantages that intelligent management of multidisciplinary joint diagnosis and treatment is achieved, medical resource configuration is optimized, diagnosis and treatment efficiency and accuracy are improved, and medical experience of patients is improved.

The invention discloses an anorectal postoperative intelligent follow-up visit management method and system based on a mobile terminal, and relates to the technical field of communication data. Comprising the steps that a dynamic follow-up survey matrix is constructed according to clinical indexes and preset weights of patients before and after an operation, and the follow-up survey matrix aims at determining the interval duration of all follow-up stages; the mobile terminal establishes a timeliness doctor-patient communication channel through the follow-up survey matrix, and the mobile terminal generates a personalized rehabilitation guidance scheme based on follow-up data. According to the method, the dynamic follow-up survey matrix is constructed, the comprehensive risk value is calculated according to the quantitative assignment of the clinical indexes and the weighting of the preset weight, and the follow-up interval is iteratively optimized in combination with the real-time recovery data, so that the precision of the follow-up period is realized, the dynamic adjustment reduces unnecessary follow-up disturbance to low-risk patients, and the follow-up efficiency is improved. Tracking frequency is enhanced for middle-high risk patients, and the defect that a traditional fixed follow-up visit mode is cut at a time is thoroughly overcome.

A system for the dynamic normalization of clinical terms and entity linking using a modular natural language processing (NLP) pipeline, consisting of: a preprocessing module configured to receive unstructured clinical text and perform operations such as de-identification, spelling correction, abbreviation expansion, and sentence segmentation; a tokenization and embedding processing unit configured to segment the preprocessed text into sub-word units and generate context-related embeddings using transformer-based biomedical language models; a processing unit for dynamic vocabulary expansion configured to update a specialized dictionary in real time by monitoring new abbreviations, acronyms, or institution-specific terms from incoming clinical data streams and feedback from clinical staff; a candidate concept generator configured to retrieve potential ontology mappings by performing an approximate nearest neighbor search via an embedding index linked to medical knowledge bases; a semantic disambiguation controller configured to evaluate candidate concepts using a graph neural network model that takes into account the local linguistic context, patient-specific metadata, and temporal relationships between entities; an ontology linking interface configured to map disambiguated entities to standardized concepts in one or more controlled vocabularies; and a feedback learning module configured to incorporate clinician corrections into the model parameters through reinforcement-based updates, thus enabling continuous self-improvement of entity normalization.

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.