25 results about "Clinical evidence" patented technology

The invention discloses an interpretable clinical decision support system based on label generation and a knowledge graph. The method comprises the following steps: based on a breast cancer domain knowledge enhanced version Qwen-BrCaAdapt of a general large language model Qwen, analyzing an unstructured medical record text of a patient, and generating a structured result containing tags, values, evidences and explanations; calculating a reasoning label through a path matching engine by utilizing an editable structured path rule table, and matching a candidate treatment scheme according to the reasoning label; and taking the matched treatment scheme as a central node, calling a medical knowledge graph to bind entity information including clinical evidence, recommendation levels, medical insurance information, medication risks, usage and dosage, and generating a traceable JSON structure and a visual report. The method has the beneficial effects that the accuracy and efficiency of tag generation in the breast cancer field are improved, the rule maintenance cost is reduced, the interpretability and traceability of a clinical decision scheme are enhanced, and the acceptability of a doctor to a recommendation result is improved.

The invention discloses a multi-agent medical intelligent diagnosis method and system based on an inference tree structure, electronic equipment and a storage medium. The method comprises the steps that a large language model is commanded to simulate multiple specialist physician roles through preset cue words; defining an inference tree structure, and requiring each agent to output a diagnosis result according to the structure; each agent analyzes the medical examination data of the corresponding field to generate a diagnosis result based on an inference tree structure; designing a multi-agent cross validation mechanism, and updating an inference tree through pairwise discussion of agents; and fusing the reasoning trees of all agents, and outputting final diagnosis including a diagnosis conclusion, a reasoning process and clinical evidence. According to the method, medical examination data in different fields are analyzed by utilizing a multi-agent method based on an inference tree structure, so that the diagnosis performance of a complex medical scene is improved.

The invention discloses a new mutation-oriented pathogenicity prediction method, which comprises the following steps of: acquiring new mutation site data to be analyzed, and generating a numerical feature vector containing evolution conservative property, function prediction score and quantitative clinical evidence; constructing a feature extractor through the numerical feature vector; inputting a training data set containing a label sample and a label-free sample into the feature extractor, and updating the network parameters of the feature extractor and each header; and predicting target variation by using the feature extractor after parameter updating and the supervised classification head to obtain a pathogenicity probability, and calculating a contribution value of each feature in the numerical feature vector to the pathogenicity probability. According to the technical scheme provided by the invention, the auxiliary decision-making information conforming to the diagnosis habit of a doctor is output, the workload of manually interpreting the variation with unclear meaning is greatly reduced, and the end-to-end intelligent processing from the original sequencing data to the clinical auxiliary diagnosis is realized.

The invention discloses an intelligent voice interaction system and method for an anesthesia awakening period, and relates to the technical field of language interaction, and the system comprises a standard demand data module which stores an anesthesia awakening period general demand category generated based on clustering analysis; the fuzzy semantic recognition module is configured to extract acoustic features of the real-time voice signals; extracting incomplete semantic segments of the real-time voice; calculating the similarity between the real-time voice and the standard voice fuzzy fragment through a fuzzy matching algorithm; the multi-modal decision module is configured to extract attitude features in combination with the real-time action signals; fusing the voice demand probability distribution, the attitude features and a standard demand database; and calculating the comprehensive confidence coefficient according to the demand category, and taking the demand tag corresponding to the highest value as the user language interaction demand. The method has the advantages that a multi-source decision-making mechanism based on clinical evidence-based medicine is realized, and the patient medical demand identification interaction accuracy when the language function of the patient in the anesthesia awakening period is limited is improved.

The invention discloses an automatic examination method and system for radiotherapy records based on a multi-modal pre-training model, and a storage medium, and belongs to the field of medical artificial intelligence and radiooncology quality control. The method comprises the following steps: acquiring and standardizing multi-modal data of the whole process of radiotherapy; extracting and fusing image, text and dose features through an improved vision-language model and an enhanced ClinicalBERT (ClinicalBERT); retrieving related clinical evidences from the radiotherapy knowledge base based on a retrieval enhancement generation framework; inputting the fusion features and the retrieval evidence into a locally deployed multi-modal model to generate a structured review report; and executing six functional modules of target coverage evaluation, organ protection analysis, DVH interpretation, quality scoring, guide compliance inspection and anomaly detection based on a report, outputting a grading decision result, and supporting full-link audit tracing. According to the invention, automatic and standardized examination of radiotherapy records is realized, the examination efficiency and consistency are improved, and the labor burden and subjective difference are reduced.

The present invention consists of a digital, always-online computer system designed to optimize perioperative safety through the automated integration of clinical and laboratory parameters. Unlike conventional medical calculators, this system implements a reactive logic engine that simultaneously calculates glomerular filtration rate (CKD-EPI 2021) and scores multiple risk scales (Goldman, LEE, ASA, CHA2DS2-VASc, HAS-BLED) without duplicate data entry. The system includes a drug reconciliation algorithm that cross-references the bleeding risk of the surgical procedure with the patient's renal function, generating accurate recommendations for discontinuing or continuing high-risk medications (anticoagulants, antiplatelet agents, SGLT2 inhibitors) based on current clinical evidence.

The invention discloses a medical history collection system supporting clinical thinking path recognition, and belongs to the technical field of medical information. The system comprises a hypothesis agent cluster management module used for initializing an agent cluster composed of a plurality of hypothesis agents based on a clinical knowledge graph, each hypothesis agent corresponding to a candidate disease and comprising static knowledge related to the candidate disease; the doctor-patient interaction interface module is used for receiving the initial clinical evidence and analyzing the initial clinical evidence into an initial clinical evidence state vector; the dynamic evidence processing and state evolution engine is used for iteratively driving the state evolution of the agent cluster; and the thinking path generation and visualization module is used for generating a clinical thinking path according to the state evolution process of the agent cluster. The technical problem that the dynamic and nonlinear diagnosis thinking process of clinicians cannot be effectively captured and restored in the prior art is solved.

Disclosed in the present invention is an intelligent chronic disease screening and health management system and method based on integration of treatment and prevention. The system comprises: an application end used for acquiring daily health data of a user, wherein the daily health data comprises conventional body indexes, daily diet data, daily medication data, and psychological health data, and the application end comprises an analysis module used for acquiring and analyzing the daily health data of the user sent by the application end, so as to obtain an analysis result; a system platform comprising a feedback module and used for carrying out expert diagnosis on the basis of the analysis result given by the analysis module, and uploading a result of the expert diagnosis; and a database used for storing health data of a healthy user and health data of an unhealthy user. The intelligent chronic disease screening and health management system and method based on integration of treatment and prevention of the present invention creates a periodic management action path for the user, continuously assists in the user in executing management items by taking the day as a unit, uses the management means following a clinical evidence-based basis, and constructs a related model to provide optimal management solutions for patients with different symptoms.

To provide improved compositions and methods for treating muscular dystrophy.SOLUTION: For example, the disclosure provides methods for treating Duchenne muscular dystrophy patients having a mutation in the DMD gene that is suited to exon 45 skipping by administering an effective amount of casimersen. The present disclosure is based on clinical evidence showing that treatment with casimersen, which is an exon 45 skipping antisense oligonucleotide, significantly increased dystrophin protein in patients over baseline. Furthermore, a positive correlation between exon skipping and de novo dystrophin protein was observed.SELECTED DRAWING: None

This invention provides a method and system for intelligent medical research topic selection based on multimodal data analysis. The invention extracts and standardizes subdivided research directions using a hierarchical departmental navigation tree, and combines UMLS and MeSH medical ontologies to assign semantic, weighted, and policy-oriented attributes to knowledge nodes, organizing them into a structured domain knowledge ontology. After fusing multi-source heterogeneous data, a hierarchical causal graph is constructed to automatically identify three types of causal edges: macro-policy, meso-academic collaboration, and micro-clinical evidence, ensuring cross-layer semantic consistency. Through multi-hop causal propagation driven by multi-attribute indicators, attribute activation paths are obtained based on dimensions of innovation, clinical need, technological frontier, and impact. Path consistency is evaluated using semantic aggregation degree, and finally, back-attribution is applied to feasibility constraints, forming a topic ranking with a traceable evidence chain. The hierarchical causal graph construction and multi-attribute independent propagation mechanism of this invention significantly improve the scientific rigor and personalized matching accuracy of research topic generation.

This invention discloses a method and apparatus for cervical lesion decision-making based on heterogeneous skill invocation and reinforcement learning. Its core lies in constructing a meta-controller driven by reinforcement learning and a decoupled heterogeneous intelligent agent skill library. The skill library encapsulates feature extraction and clinical logic into independent skills such as multi-instance learning visual perception, retrieval enhancement guide alignment, and safety backoff. The meta-controller models clinical reasoning as a Markov decision process, dynamically outputting skill scheduling instructions based on the slot state of the evidence graph and the model's cognitive uncertainty. This invention employs an action masking mechanism and a joint reward function including pathological consistency, multi-instance frame selection concentration, and invocation cost penalties to optimize the meta-controller's offline strategy, and introduces behavioral cloning constraints to inhibit out-of-distribution exploration. Finally, it synchronously outputs disease risk prediction and a traceable clinical evidence chain composed of structured skill invocation trajectories, achieving highly transparent and secure medical assistance decision-making.

The application discloses a pathogenicity prediction method for novel variation, comprising the following steps: obtaining novel variation site data to be analyzed, and generating a numerical feature vector containing evolutionary conservation, functional prediction score and quantitative clinical evidence; constructing a feature extractor through the numerical feature vector; inputting a training data set containing labeled samples and unlabeled samples into the feature extractor, updating the feature extractor and the network parameters of each head; applying the feature extractor and the supervised classification head after parameter updating to predict target variation, obtaining pathogenicity probability, and calculating the contribution value of each feature in the numerical feature vector to the pathogenicity probability. The technical scheme provided by the application outputs auxiliary decision information conforming to the diagnosis habit of doctors, greatly reduces the work load of artificial interpretation of unknown variation, and realizes end-to-end intelligent processing from raw sequencing data to clinical auxiliary diagnosis.

The invention provides a sequencing data analysis method and device, computer equipment and a program product, and is applied to the computer equipment, and the method comprises the following steps: the computer equipment obtains sequencing data of a to-be-analyzed sample, the sequencing data comprises one or more variation sites and variation information corresponding to each variation site, and the variation information corresponds to the variation sites; the variation information comprises identification information corresponding to variation sites; the computer equipment searches associated information corresponding to each variation site in a database according to the identification information corresponding to each variation site, and the associated information comprises one or more of the report frequency of the variation site, the clinical evidence level of the variation site and the first interpretation content of the variation site; and the computer equipment displays the associated information and / or variation information corresponding to each variation site. According to the method and the device, automatic association interpretation of a plurality of variation sites can be realized, and the efficiency and the quality of analyzing the sequencing data are improved.

The invention provides a construction method of a diagnostic factor. The construction method comprises the following steps: a data acquisition and standardization step; obtaining multi-source heterogeneous clinical data of the subject; the clinical data comprises unstructured text data and structured data; performing standardization processing on the clinical data; an evidence aggregation and factor generation step; performing entity recognition and semantic analysis on the unstructured text data by adopting a natural language processing technology, extracting clinical term entities, and mapping the clinical term entities to a standard medical term system to form standardized clinical term entities; the structured data and the standardized clinical term entities form clinical evidences; and collecting the clinical evidence of the subject to the corresponding diagnostic factor based on a predefined diagnostic factor template library to obtain the diagnostic factor of the subject.