1000 results about "Medical science" patented technology

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 an intelligent diagnosis and treatment method and system based on a knowledge graph and a large language model. The method comprises the steps of constructing a basic framework of a medical field knowledge graph based on collected medical field data, constructing multi-level knowledge graph expression, obtaining symptom description of a patient through multiple rounds of dialogues, diagnosing the patient and generating a diagnosis report, rechecking and adjusting the diagnosis report by a professional physician, and finally outputting the diagnosis report. The intelligent diagnosis and treatment system comprises a knowledge graph construction module, an intelligent question and answer system, an entity matching module, an intelligent diagnosis module, a doctor review and diagnosis report adjustment module, a diagnosis report output module and the like. And main diseases are screened, so that the coverage is wider. And three levels of knowledge maps are constructed, so that text matching with finer granularity can be carried out. The non-standardized expression of the patient is firstly converted into standardized medical terms through a large model, and drug allergy and the like are taken into consideration, so that the diagnosis accuracy is improved, and the diagnosis and treatment risk is reduced.

The invention discloses a medical history recognition method and system based on a large model and an expert strategy, and the method comprises the steps: collecting data in a medical network, and carrying out the cleaning and standardization processing of the data; extracting medical entities and relationships thereof from the text, and optimizing the rule set; the extracted entities and relations are fused into a knowledge graph, a multi-relation graph is constructed, and attributes are defined; storing the knowledge graph in a graph database, and receiving new data through a real-time data stream processing system; basic information and symptom description of a patient are collected, and key information is extracted by using a large language model; an inquiry path is dynamically adjusted, and specific symptoms are clearly and fuzzily answered or deeply discussed by questioning; and generating a structured electronic medical record by using the large language model. Through an intelligent pre-inquiry process, the system can collect detailed medical history information and generate a standardized electronic medical record before a patient officially sees a doctor, so that the information verification and supplement time when a doctor receives the doctor is greatly shortened.

The invention provides a feature fusion method and system based on multi-modal medical data, and belongs to the technical field of medical data processing. According to the method, the structural relationship, such as a patient network, a biological pathway and an anatomical structure, in the medical data is modeled by using # imgabs0 #, the extracted structured features are input into # imgabs1 # to capture global context information, and a self-adaptive mechanism can dynamically adjust the feature weights output by # imgabs2 # and # imgabs3 # according to the data modality, so that more accurate feature fusion is realized. Experimental results prove that the model provided by the invention is superior to other baseline models on a plurality of multi-modal medical data sets.

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 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 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 relates to the technical field of medical care, in particular to an intelligent query method for a nursing knowledge base, which comprises the following steps of: performing multi-form standardized processing on a nursing problem input by a user, deeply analyzing a query request by utilizing a semantic understanding model, and obtaining an intelligent query result based on an inference algorithm; the method comprises the following steps: automatically selecting related literatures, clinical data and nursing experience data from a nursing knowledge base, generating a preliminary nursing scheme candidate set in combination with a nursing professional knowledge graph, and performing personalized screening and optimization on the scheme by using an intelligent recommendation model in combination with the current illness state, treatment progress and historical nursing data of a patient; the intelligent recommendation model is self-optimized through user feedback, and the nursing scheme recommendation precision is continuously improved. The nursing knowledge base automatically fuses newest medical research results and clinical nursing practice data. The problems that in the prior art, nursing scheme recommendation accuracy is low, updating is not timely, and the individuation degree is insufficient are effectively solved.

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

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

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

The invention discloses a diagnosis accompanying service-oriented dynamic user portrait generation and personalized recommendation method, particularly relates to the technical field of medical portrait personalized recommendation, and is used for solving the problem of insufficient user demand dynamic adaptability caused by recommendation homogenization and portrait update stiffness due to excessive dependence on historical behavior data in an existing recommendation system. A dynamically evolved user portrait is constructed by fusing time sequence characteristics of a user behavior sequence, time-space fluctuation characteristics of physiological indexes and spatial semantics of a doctor seeing environment, and the limitation that a traditional recommendation system is disjointed from medical logic in a medical scene is broken through through closed-loop optimization of a disease-service mapping rule and real-time feedback, so that the medical scene recommendation system is more accurate. Co-evolution of demand perception and resource scheduling is realized; the recommendation strategy based on dynamic weight fusion and diversity threshold screening gives consideration to accuracy of service matching and exploratory performance of potential requirements, and recommendation reliability and service response efficiency in a complex medical scene are effectively improved.

Various systems and methods are provided for generating and editing of medical reports and records using artificial intelligence (AI) assistance. An intelligent medical reporting tool may edit medical materials such as electronic medical records, medical reports, and treatment plans. The intelligent medical reporting tool can provide edit recommendations and suggested additions to medical materials to a user of the tool. Edit recommendations and suggested additions may be related to contradictions, incompleteness, clarity, and clinical guidelines.

The invention discloses a medical knowledge constrained multi-modal time series data dynamic evaluation method and a wearable medical system. According to the method, a mixed perception architecture is constructed through an improved Swin-Transform time sequence encoder and a graph neural network, a medical priori mask matrix is embedded to constrain an attention mechanism, and time sequence feature extraction and cross-parameter correlation modeling of multi-modal physiological signals are enhanced; a dynamic quality evaluation model is designed, data integrity, cross-modal consistency and diagnosis effectiveness indexes are fused, an evaluation weight is adjusted in real time in combination with an adaptive weight function, and quantitative association of data quality and clinical diagnosis confidence is achieved; and when the motion artifacts or pathological conflicts are judged to exist, starting an abnormal positioning and correcting mechanism. According to the method, noise such as motion artifacts and interference can be monitored at any time, the attention to different indexes is adjusted in real time according to the risk level, and the pathological recognition accuracy and clinical credibility are improved by fusing multi-mode perception of medical knowledge.

The invention provides a common mental disease intelligent diagnosis method and system based on multiple agents, and relates to the field of medical artificial intelligence. The method comprises the following steps: S1, extracting diagnosis standards and symptom characteristics of common mental disorders, and constructing a similar patient knowledge base after verification and evaluation of the similar patient knowledge base and expert calibration; s2, acquiring clinical data from a hospital information system, and performing large medical record structuring, clinical scale simplification and scale score analysis on the clinical data to form a similar patient database; and S3, performing symptom matching and scale performance analysis on the input clinical data, constructing a multi-agent mental disease diagnosis framework, and performing multi-agent diagnosis debate based on the multi-agent mental disease diagnosis framework. The technical problems of symptom overlapping and diagnosis subjectivity among common mental diseases are solved by constructing a multi-agent cooperative diagnosis framework, introducing particle size symptom analysis and dynamically integrating structured authoritative medical diagnosis standards.

The invention provides a clinical auxiliary decision-making method based on artificial intelligence and a related product, and the method comprises the steps: building a knowledge graph covering medical entities and relationships through learning authoritative medical data and medical records; a doctor inputs patient information, and data standardization and cleaning are carried out; based on the multi-dimensional information of the patient, recommending suspected diagnosis and differential diagnosis; recommending a preset examination scheme according to the suspected diagnosis and differential diagnosis results; when a doctor's advice is issued, the system checks conflicts between inspection and inspection items in real time and provides risk prompts; similar medical records are recommended based on patient clinical information, and diagnosis and treatment reference is provided for doctors. The clinical aid decision-making method based on artificial intelligence and related products have the beneficial effects that the disease list of suspected diagnosis and definite diagnosis is generated by automatically analyzing the illness state of the patient, and examination, inspection and treatment schemes are recommended in a targeted manner, so that the problems of missed diagnosis and misdiagnosis are effectively avoided.

Agentic AI, Contextual AI, and Generative AI systems and methods are provided for quantitative or qualitative patient analytics, blood work analytics, medical image analytics (x-ray, ultrasound, CT Scan, MRI and the like), brain scan analytics, present or historical genetic analytics and the like, to be used to guide, assist or even replace some of these physician functions with improved patient outcomes or with an improved understanding of the source of ailments, the analysis of treatment efficacy or treatment side-effects for individuals or populations of individuals. Genetic information is used to create individualized or population-based analysis, diagnostics, or treatment plans with even better outcomes.

The invention discloses a medical education-oriented knowledge graph construction, retrieval and application system, and the system comprises a data collection and integration module which is used for collecting and integrating various medical professional knowledge, and forming a medical knowledge material library; the intelligent algorithm module is used for constructing a knowledge graph according to the medical knowledge material library and then providing structured knowledge support for learning and reasoning of the knowledge graph by using a graph neural network model; the knowledge retrieval and content generation module is used for performing retrieval in the knowledge graph according to the input content and providing the retrieved knowledge to the large language model to obtain the corresponding content; and the knowledge application service module is used for providing medical education service according to learning and understanding of the graph neural network model on the knowledge graph and integration of the big language model on semantic information in the knowledge graph. The intelligent development of the medical field is better promoted, and more efficient and accurate support and service are provided for medical education, research and the like.

An online medical question and answer dynamic retrieval enhancement generation method relates to the technical field of large language models, and by introducing semantic entropy and a semantic entropy-based dynamic threshold setting mechanism, a system determines whether to trigger retrieval according to a semantic entropy value of generated content. According to the method, excessive retrieval operations brought by a traditional static threshold method are avoided, the retrieval pertinence is improved, the professionalism and accuracy of answers are enhanced, and the possibility that the model generates'illusion 'or irrelevant answers is reduced. Medical entity extraction is carried out in combination with a special model (Med-BERT) in the medical field, and the medical elements in the text can be accurately recognized and generated in a classified mode. Based on the medical elements, a more accurate judgment basis can be provided for triggering of a system retrieval mechanism.

Devices, systems, methods, and software for providing remote medical diagnosis and therapy to a subject comprising: a module for conducting telecommunications with a telemedicalist; a module for applying a diagnostic or a therapeutic analysis; an apparatus for dispensing one or more medical items from an inventory of medical items, the inventory of medical items risk profiled to a subject, a population, a venue, or a situation; and optionally, a biosensor apparatus.

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

The invention discloses an X-ray chest radiography report automatic generation method, system and device based on factual description enhancement and a medium. The method comprises the following steps: firstly, constructing a medical entity extraction method based on a RadGraph model, and carrying out identification and structured extraction on clinical keywords to obtain factual description consisting of key medical entities; secondly, establishing a comparative learning method guided by factual description, enhancing semantic consistency between the image and the text from global and local levels, and extracting visual features with diagnostic value; establishing a historical similar case retrieval strategy independent of disease tags, and calculating visual semantic similarity to realize automatic retrieval of historical cases; and finally, proposing an evidence-driven chest radiography report generation method, constructing a cross-modal fusion network, and generating a chest radiography report with clinical accuracy and consistency. The system, the equipment and the medium automatically generate an X-ray chest radiography report based on factual description enhancement based on the method; according to the method, efficient and stable automatic retrieval is realized, the clinical accuracy of the generated chest radiograph report and the reliability of evaluation are improved, and the universality and robustness of the model are remarkably improved.

An AI-powered system is disclosed for conducting medical interviews, generating comprehensive medical history reports, and providing clinical insights. The system leverages a large language model (LLM) fine-tuned on domain-specific medical datasets to generate interview prompts, iteratively refine responses based on natural language processing (NLP), and identify incomplete or inconsistent information. The system provides patients with education specific to their situation and tailored to varying levels of health literacy. The system ensures compliance with data protection regulations such as HIPAA and GDPR by employing advanced encryption techniques, multi-factor authentication, and secure data storage. The generated medical history report includes a structured clinical summary, and may include provisional diagnoses with associated ICD-10 codes, clinical analysis, suggestions for diagnostic evaluation and treatment, and other pertinent information. Additional features include EHR integration, customizability, scalability, multilingual support, bias mitigation techniques, and adaptability to diverse demographic groups, ensuring equitable and inclusive healthcare delivery.

The invention relates to the technical field of medical diagnosis processing, and discloses an intelligent disease diagnosis and differential diagnosis system based on a knowledge graph, and the system comprises a data input module which is used for receiving and standardizing clinical symptoms, signs, laboratory examination data and historical medical record data of a patient; the knowledge graph construction and updating module is used for constructing and updating a knowledge graph of diseases and symptoms according to the medical literature and the clinical data, and the knowledge graph automatically extracts an incidence relation between the symptoms and the diseases from the medical literature through a natural language processing technology; and the reasoning and diagnosis module is used for performing intelligent disease diagnosis and differential diagnosis. According to the method, by optimizing reasoning path selection and information gain calculation, the path with the most information content is selected from multiple reasoning paths for diagnosis reasoning, and the most representative path is selected by calculating the correlation degree between each symptom and the disease.

The invention provides a medical examination data analysis system and method based on artificial intelligence. The method comprises the steps of obtaining a time sequence data matrix of multiple examination indexes of a target patient at different time nodes through medical examination data of the target patient; labeling pathological labels of various examination indexes in the medical examination data, and determining medical semantic association features among different examination indexes of the target patient through the pathological labels; according to the medical semantic association features and the time sequence data matrix, determining a time sequence association relationship of different inspection indexes of the target patient on a pathological level, and generating a fusion feature vector of the health state of the target patient according to the time sequence association relationship; and an abnormal evolution feature of the current health state of the target patient is obtained by combining an abnormal detection model with the fusion feature vector, and then an auxiliary analysis result of the pathological risk of the target patient is output to medical personnel. By adopting the scheme of the invention, the dynamic change trend analysis of the disease course risk state of the patient can be realized based on the medical semantic perception ability.

The invention discloses a multi-modal diagnosis and treatment scheme recommendation system based on reinforcement learning, and relates to the technical field of artificial intelligence, and the system comprises a data processing module which is used for obtaining multi-modal medical data of a patient and carrying out the preprocessing of the multi-modal medical data to obtain a multi-modal data set; the feature extraction and fusion module is used for performing feature extraction and fusion based on the multi-modal data set to obtain multi-modal features as the current state of the patient; the training set acquisition module is used for constructing a decision network and inputting the current state of the patient to obtain a current diagnosis and treatment scheme, taking the current state of the patient and the current diagnosis and treatment scheme as data groups, and circulating the process to obtain a plurality of data groups to form a training set; the network optimization module is used for optimizing the decision network based on the training set and the core objective function to obtain an optimized decision network; and the optimal scheme output module is used for obtaining the current state of the patient to be recommended and inputting the current state into the optimized decision network to obtain an optimal diagnosis and treatment scheme. And the disease diagnosis accuracy and comprehensiveness are improved.

The invention discloses an intelligent patient monitoring and predicting clinical data analysis system, which belongs to the field of medical analysis and comprises a real-time monitoring module, a data acquisition module and a knowledge base building module. According to the method, the nursing quality of the patient can be improved, medical resources can be effectively saved, the processing capacity of complex medical data is enhanced, the scientificity and rationality of clinical decisions are improved, a personalized and timely health management scheme is provided for the patient, a hospital can be more prospective and initiative in disease management, and the disease management efficiency is improved. Therefore, the overall medical service level is improved, uncertainty and patient individualized demand differences existing in clinical data can be effectively handled, and scheme suggestions with higher diversity and flexibility are provided for medical staff during decision making, so that the whole system has real-time performance, and long-term effect optimization across time dimensions can be realized; the treatment effect and experience of the patient can be improved, and a scientific basis is provided for reasonable distribution of medical resources.

The invention relates to a professional knowledge graph construction method for the medical field, and the method comprises the following steps: obtaining a multi-modal medical document, carrying out the deep analysis through a Transform-based multi-modal model, and obtaining an analyzed multi-modal medical document; dynamically segmenting the analyzed multi-modal medical document by adopting a context-based adaptive text segmentation method to obtain independent text blocks; based on the independent text blocks, performing structured extraction, and performing verification by adopting a dynamic dual-verification mechanism to form knowledge sub-graphs; and based on the knowledge sub-graph, further constructing a standardized medical knowledge graph. Compared with the prior art, the method has the advantages of high precision, expandability and the like.