20 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 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 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.

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.

A clinical documentation improvement (CDI) smart scoring method may include predicting, via per-condition diagnosis machine learning (ML) models and based on clinical evidence received by a system, a probability that a medical condition is under-documented and, via per-condition documentation ML models and based on documentation received by the system, a probability that a medical condition is over-documented. The under- and over-documentation scores are combined in view of special indicators and queryability factors, which can also be evaluated using ML query prediction models, to generate an initial CDI score. This CDI score can be further adjusted, if necessary or desired, to account for factors such as length of stay, payer, patient location, CDI review timing, etc. The final CDI score can be used to prioritize patient cases for review by CDI specialists to quickly and efficiently identify meaningful CDI opportunities.

The present invention provides clinical evidence for a method of stem cell transplantation that facilitates engraftment and reconstitutes immunocompetence of the recipient without requiring myeloablative conditioning.

A method for automated real-time processing of clinical evidence, implemented by a computing system includes receiving a user input that contains a clinical question; generating parameters for a Population, Intervention, Comparison, Outcome (PICO) table using a large language model (LLM) relevant to the clinical question; retrieving clinical research studies from multiple databases based on the parameters as approved by a user electronic device; screening these studies using two independent artificial intelligence (Al) systems that analyze titles and abstracts or executive summaries; resolving discrepancies between the Al systems via a researcher audit interface; and generating a list of clinical research studies based on the screenings.

According to the chronic atrophic gastritis clinical prediction model based on the random forest algorithm, the SHAP method is used for explaining and illustrating feature importance and explaining the prediction result of the model so as to determine relevant risk factors of the CAG, and reliable clinical evidence is provided for screening high-risk groups before endoscopic examination of the CAG. Meanwhile, the invention further provides a visual webpage platform based on a Shiny framework, namely a chronic atrophic gastritis clinical prediction platform based on a random forest algorithm, a doctor can visually obtain an individual CAG risk prediction value, operation is easy and convenient, practicability is high, and clinical application and popularization are facilitated.

The invention discloses a cervical cytopathy degree scoring method based on TBS diagnostic standard quantification, and relates to the problem that the cytopathy degree is difficult to automatically detect in a cervical cytopathic auxiliary diagnosis technology. According to the cervical cell pathology auxiliary diagnosis technology, the overall lesion condition of the cervical cell smear is automatically evaluated through the image processing technology. However, a traditional scheme depends on implicit feature extraction based on an encoder, the interpretability is poor, and evaluation indexes and effective clinical evidences accepted by pathologists are difficult to provide. In order to improve the problem, the invention provides a cervical cell lesion degree scoring method based on TBS diagnostic standard quantification. Experiments prove that the method can be used for effectively extracting lesion characteristics such as nuclear cytoplasmic ratio, nuclear deep staining and nuclear roundness of cells, and effectively scoring the degree of cytopathy according to the characteristics. The method is applied to cervical cell lesion feature extraction and pathological interpretability lesion degree scoring.

The invention specifically discloses a medical literature citation link tracking method and system, relates to medical health information processing, and solves the problems of large citation network scale, data isomerism, incomplete metadata analysis and low tracking efficiency in clinical evidence-based decision and drug safety monitoring. The method comprises the steps that heterogeneous medical literatures are collected and normalized, and evidence-based medical evidence levels are marked; analyzing the reference literature list, and complementing the DOI / PMID metadata; constructing a reference topology network of the graph database, clustering nodes by using a MeSH tree hierarchical structure, and extracting PICO elements; citation intensity is calculated by integrating citation frequency, position weight, context motivation, evidence level and PICO similarity, and key paths are screened; traceability and diffusion tracking are supported, draft withdrawal events and adverse drug reaction signals are dynamically monitored, and real-time early warning is updated through streaming increment. According to the method, visual tracking and risk assessment of the reference relationship of the medical literature are realized, and efficient and accurate evidence tracing support is provided for clinical evidence-based decision and drug safety assessment.

The invention discloses a preparation method of a detoxification and leucorrhea arresting mixture, the mixture prepared by the method, and a quality control method and application of the mixture. The mixture is characterized in that in every 1mL of the mixture, the content of psoralen (C11H6O3) is not less than 14.89 [mu] g, and the content of astilbin (C21H22O11) is not less than 0.54 mg. The preparation method comprises the integrated steps of extracting medicinal materials, concentrating an extracting solution until the relative density is 1.06-1.10 at the temperature of 60 DEG C, standing and purifying, and adding stevioside and sodium benzoate in a specific proportion to prepare liquid and form. The invention also establishes a comprehensive quality control system covering thin-layer chromatography identification of multiple medicines including ovateleaf holly bark, pericarpium citri reticulatae, liquorice and plantain herb and content determination of the two-index components. In-vitro and clinical evidences show that the mixture has antibacterial and anti-inflammatory effects and has a good application prospect in treating gynecological diseases related to human papilloma virus (HPV) infection.

The invention relates to the field of clinical pathogens, and provides a machine learning-based clinical pathogen infection interpretation method, which comprises the following steps of: acquiring original sequencing data, removing low-quality read segments, removing noise comprising a carrier sequence and a linker sequence by using a bioinformatics tool, and standardizing all data into an FASTQ format to obtain cleaned data; comparing the cleaned data to a reference genome, identifying single nucleotide variation, insertion deletion, single nucleotide polymorphism and copy number variation in a sample, annotating a sequencing read section, and identifying a known pathogen gene and a drug-resistant gene; judging the type of an infected pathogen according to a preset rule in combination with a generation signal pre-judgment result, amplicon coverage, pathogen pathogenicity, a strong positive ortho-position, a sample type and clinical evidence; and training the model, inputting the processed features of the to-be-tested sample into the optimized model, and outputting a pathogen infection interpretation result. When the kit is used, the accuracy and efficiency of clinical pathogen infection interpretation are improved.

The invention belongs to the technical field of medical data processing, and particularly relates to an algorithm library matching system and a difference calculation method for a complex chronic common disease scene, and the system comprises a hierarchical algorithm library construction module which is used for constructing a common disease algorithm library and realizing the structured storage of an algorithm module; meanwhile, relying on a real-time updating interface, newest clinical evidence and diagnosis and treatment practice data are accessed, and algorithm rules of all levels are dynamically optimized; and the multi-dimensional matching module is used for completing algorithm matching by utilizing three-stage matching and ensuring that a matching result covers disease generality, interaction characteristics and individual differences. According to the method, the hierarchical algorithm library of the three-level architecture of the base layer, the interaction layer and the individual layer is constructed, and the multi-dimensional matching module is combined with the extraction of the disease interaction characteristics and the accurate matching of the interaction rule module, so that the interaction relationship among multiple diseases is effectively incorporated, and the diagnosis and treatment scheme conflict caused by the matching of a single disease algorithm is avoided.

The invention provides a dynamic disease analysis method and device based on a knowledge graph, equipment and a medium, and relates to the technical field of artificial intelligence and biomedicine, and the method comprises the steps: determining an initial entity relationship set from target disease data based on a general large language model and a disease evolution hierarchical mode; performing semantic alignment on the initial entity relationship set to obtain a target entity relationship set corresponding to the initial entity relationship set; constructing a disease evolution knowledge graph based on the target entity relationship set; and determining a dynamic disease analysis result based on the disease evolution knowledge graph and a preset evidence library. According to the technical scheme, entities and relations are extracted through a disease evolution layering mode, automatic evolution along with clinical new evidences is facilitated, and the generated disease evolution knowledge graph can capture the dynamic evolution rule of the disease.