475 results about "Clinical decision" patented technology

The invention provides a construction method of a comprehensive intelligent early warning system for a traumatic patient, and relates to the technical field of medical artificial intelligence, and the method comprises the steps: carrying out the processing of multi-modal data through a feature engineering framework, and extracting multi-scale time sequence features and correlation features; carrying out abnormal state identification and complication risk prediction by adopting a deep learning prediction framework, and carrying out feature fusion through an attention mechanism; constructing a medical knowledge graph to obtain vector representation of domain rule knowledge; and performing integrated verification through a bidirectional mapping mechanism to generate a prediction result. According to the invention, accurate early warning of disease development of the trauma patient can be realized, the early warning accuracy and interpretability are improved, and effective support is provided for clinical decision making.

The invention relates to the technical field of medical big data in artificial intelligence, in particular to a tumor knowledge base construction system and method based on big model and cue word optimization. The system comprises a data acquisition module, a cue word generation and optimization module, an entity recognition and relation extraction module, a knowledge graph construction module, a clinical decision support module and a knowledge base update and maintenance module. According to the method, through the cue words which are initially designed and continuously optimized, it is ensured that required information is accurately and efficiently extracted by a follow-up large-scale language model LLM, the large-scale language model LLM is used for rapidly and accurately extracting the correlation relation between the related entities from the target text information, so that an accurate hierarchical tumor knowledge map database is constructed, and the accuracy of the hierarchical tumor knowledge map database is improved. When the hierarchical tumor knowledge map database is applied subsequently, a personalized recommended treatment scheme clinical report can be generated by inputting clinical data of a target patient, so that better decision support is provided for medical staff.

The invention relates to the technical field of medical data management, in particular to a medical data document classification and marking system which comprises a document structure analysis module, a semantic information identification module, a classification and marking module and a document intelligent label module. According to the method, document visualization and retrieval efficiency is improved by analyzing document content and extracting titles, texts and appendixes, analytic performance and operability of data are enhanced by identifying key information and semantic relations and accurately extracting and labeling medical terms, medicine research data and medical record records, storage is optimized according to priorities in combination with classification and marking, and the method is high in practicability and easy to popularize and use. Clinical values and application scenes of documents are clarified, document management processes are refined, management compatible with clinical data formats is formed through structured storage of marked data and classification label generation, storage normalization and retrieval efficiency are improved, the requirement for accurate information calling in clinical tests is met, document management capacity is improved, and the document management efficiency is improved. And support is provided for improvement of clinical decision-making and research efficiency.

Described is a system for receiving a disease specific treatment algorithm corresponding to a disease of a patient from a first Electronic Health Record (EHR) system, processing data corresponding to the disease specific treatment algorithm using a Large Language Model (LLM), receiving one or more CQL models from the LLM based on the processing of the data corresponding to the disease specific treatment algorithm, receiving an update to a patient record of the patient from the first EHR system, based on the update to the patient record and the one or more CQL models, triggering a Clinical Decision Support (CDS) hook to generate an alert, and causing transmission of the alert for a medical practitioner associated with the first EHR system.

The invention discloses an accurate health risk early warning analysis system and method based on multi-modal medical data fusion. The system comprises a multi-source data acquisition module, a preprocessing module, a dynamic fusion module, a risk assessment module, an interpretability module and a dynamic early warning module. According to the method, multi-modal data are collected, feature vectors are generated through preprocessing and cross-modal fusion, a comprehensive health risk index is calculated through a double-flow model (time sequence LSTM + static GNN), abnormal association is analyzed in combination with causal reasoning, a threshold value is dynamically adjusted, grading early warning is triggered, and finally the model is optimized through reinforcement learning. According to the scheme, deep fusion and dynamic evaluation of multi-modal data are achieved, the accuracy, timeliness and interpretability of risk early warning are improved, the method is suitable for scenes such as chronic disease management and intensive care, and powerful support is provided for clinical decision making.

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

The invention discloses a benign and malignant nodule grading evaluation system based on large model fusion ultrasonic imaging and thyroid gene markers, which can organically fuse non-invasive examination and serological detection, can simulate and diagnose multi-grade risk probability information provided by a gold standard, realizes similar risk grading estimation in a non-invasive mode, and has a wide application prospect. The thyroid nodule risk assessment method can provide visual explanation conforming to clinical logic based on comprehensive information of iconography and molecular biology, can significantly improve the accuracy of thyroid nodule risk assessment, can also effectively improve clinical decision-making efficiency and patient credibility, and has important clinical application prospects. The system comprises a data acquisition module, an ultrasonic image feature extraction module, a gene marker feature extraction module, a multi-modal fusion and hierarchical reasoning module and a generation module.

The invention relates to the technical field of medical big data intelligent analysis, and discloses a medical big data intelligent analysis method and system based on deep learning, and the method comprises the steps: obtaining patient image data, case text data and wearable equipment monitoring data, carrying out the preprocessing, and carrying out the standardized coding of each modal data; and calculating the dynamic weight of each modal data, and synthesizing a multi-level fusion weight. And in combination with the fusion weight and the clinical state parameters of the patient, constructing a modal value-clinical urgency degree two-factor resource decision model to obtain a priority score. According to the priority scores, the patients are divided into different emergency treatment levels, and different treatment strategies are provided. The model provided by the invention can maintain efficient prediction capability in various clinical scenes, improves the accuracy of clinical decisions, and enables the resource allocation of hospitals to be more scientific and reasonable. In an emergency treatment environment, the real-time priority evaluation of the patient can significantly improve the efficiency of emergency treatment, reduce the waiting time, and improve the survival rate and the treatment effect of the patient.

The invention discloses a medical clinical decision support method and system based on a knowledge graph, and the method comprises the following steps: S1, collecting structured and unstructured medical data, and constructing an initial medical knowledge graph; s2, performing term standardization and semantic alignment on the graph to generate a fusion knowledge graph; s3, constructing a time-labeled medical record graph structure based on the medical record data, and aligning the time-labeled medical record graph structure with the fusion graph; s4, inputting the fusion atlas and the medical record graph into the hypersphere graph neural model, and generating semantic representation; s5, calculating a gravitation vector by using a path traction module, and guiding the propagation direction of the reasoning path; s6, generating a diagnosis and treatment candidate set and a corresponding recommended path according to the node state; s7, optimizing a model structure and initial parameters through a black widow spider optimization algorithm; and S8, outputting diagnosis and treatment suggestions and reasoning paths. According to the method, intelligent organization of medical knowledge and accurate diagnosis and treatment path recommendation are realized, and the auxiliary decision making efficiency and reliability are improved.

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

The invention relates to a data fusion processing method and system for an intelligent sensor based on MEMS, and the method comprises the following steps: carrying out the non-stationary signal decomposition of a multi-modal physiological parameter time sequence data stream, and obtaining a key vital sign feature sequence; performing phase coupling degree calculation on the key vital sign feature sequence to obtain an inter-organ function correlation intensity matrix; performing high-dimensional feature space projection on the critical patient based on the inter-organ function association intensity matrix to obtain a pathological state feature map of the patient; performing nonlinear kinetic analysis on the pathological state characteristic spectrum of the patient to obtain a disease progress dynamic trajectory; and performing clinical decision support reasoning based on the disease progress dynamic trajectory to obtain personalized treatment suggestions, thereby solving the technical problem that the existing analysis method is often limited to observation of a single physiological parameter or simple statistical analysis and lacks the ability to understand the disease mechanism from the overall perspective.

The invention relates to the technical field of intelligent medical treatment, and discloses an AI-based severe patient sepsis early warning method and system. According to the method, a multi-organ interaction mechanism is deeply analyzed by dynamically constructing an organ-level causal network, and early-stage accurate early warning of sepsis is realized: high-frequency physiological waveforms, asynchronous laboratory indexes and treatment intervention data are fused, and the capture ability of microcirculation failure and autonomic nerve decline is improved; the early warning threshold value is dynamically adjusted based on the treatment coverage degree, and the delay and false alarm defects of a fixed threshold value mechanism are effectively overcome; a pathogen targeted therapy map and an organ function support scheme are automatically generated by a graded triggered clinical action chain, and the clinical decision response time is shortened; according to the method, the risk of missed diagnosis is reduced while the early warning sensitivity is improved, and an earlier and more reliable intervention window is provided for critical patients.

The invention discloses an enteritis patient condition severity risk early warning method, a medium and equipment. The method comprises the following steps: acquiring a borborygmus original signal and a clinical multi-dimensional physiological parameter sequence through a sensing device; constructing a borborygmus dynamic characteristic spectrum based on the borborygmus original signal to generate an acoustic biomarker time sequence; inputting the acoustic biomarker time sequence and the clinical multi-dimensional physiological parameter sequence into a multi-modal fusion early warning model to obtain an intestinal inflammation risk index; executing a signal quality self-evaluation process and generating a data quality warning code when the signal quality is abnormal; triggering a multi-node collaborative monitoring mechanism based on the risk index to generate an intestinal state multi-dimensional situation map; establishing an individualized risk baseline and generating a graded early warning instruction; and finally outputting a comprehensive early warning report. According to the method, multi-modal fusion analysis of the borborygmus signal and the clinical parameters is realized, the accuracy and timeliness of illness state early warning are remarkably improved through dynamic risk assessment and signal quality monitoring, and a reliable basis is provided for clinical decision making.

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.

A computer-based adaptive decision support system for radiotherapy, including: • a patient data acquisition module configured to acquire patient-specific clinical data, including an anatomical image, a physiological signal, and genomic data; • a preprocessing and feature extraction modality compatible with normalizing, preprocessing and extracting statistical features from the acquired data; • a status estimator used to provide a dynamic representation of the patient's treatment evolution status based on radiation, biological, dosimetric characteristics; • customized action rescaler to define a series of clinically meaningful treatment adjustments depending on the patient's current condition; • a reward function engine used to calculate therapeutic outcome scores based on the probability of tumor control, the probability of complications in normal tissue, and other predetermined factors; • a reinforcement learning agent that can learn and update treatment adaptation policies based on deep reinforcement learning techniques; • a clinical decision dashboard that provides recommended treatment adjustments and personalized interaction with the physician; and • a clinical integration interface adapted for exporting the customized treatment plan to an external treatment planning or delivery system.

The invention discloses a prognosis evaluation method and system for II-III stage colorectal cancer patients, and relates to the technical field of wisdom medicines.The prognosis evaluation method comprises the steps that variables related to survival prognosis of the patients are screened, potential variables influencing RFS and OS of the patients are obtained, and prognosis outcomes of the colorectal cancer patients are evaluated by evaluating relevance between various indexes and prognosis outcomes of the colorectal cancer patients and correcting confounding factors; screening out indexes having important value for the model; the screened indexes and machine learning algorithms are utilized to construct RFS and OS prognosis models of the CRC patients, the RFS and OS prognosis models are used for performing prognosis evaluation on the CRC patients in the II-III stages, and the machine learning algorithms comprise six types of LR, RF, XGB, SVC, MLP and GNB. By integrating clinical data and a machine learning technology, an efficient and accurate tool is provided for prognosis evaluation of the II-III stage colorectal cancer patient, and a scientific basis is provided for making clinical decision support and an individualized treatment scheme.

The invention provides a multi-dimensional medical data analysis and auxiliary decision-making system and method. The system comprises a data acquisition module, a preprocessing module, a feature extraction module, a model training module, a multi-dimensional analysis module, a decision-making generation module and a feedback optimization module. The data acquisition module acquires data from multiple sources; the preprocessing module performs data cleaning and standardization; the feature extraction module extracts multi-dimensional features; the model training module trains an adaptive decision model; according to the invention, scientificity, accuracy and reliability of medical decision making can be improved, and powerful support is provided for clinical decision making.

The invention discloses an acute kidney injury risk early warning system, and relates to the technical field of clinical decision support systems. In order to solve the problems that in the prior art, due to the fact that a large amount of data preprocessing and model training need long-time data processing and model operation, the early warning opportunity is not accurate, the real-time performance is low, personalized early warning prompting cannot be carried out for patient groups, and early warning fatigue and early warning desensitization are likely to be caused. Through data preprocessing, model construction and risk analysis, real-time monitoring and accurate prediction of the AKI risk of a patient are realized, the early recognition rate is improved, the early warning decision unit generates specific early warning information according to risk scores and kidney function change rules and provides targeted treatment suggestions, and meanwhile, through an early warning threshold adjustment and approach matching module, the early warning decision unit can perform early warning on the AKI risk of the patient. The early warning strategy is optimized, the response speed of medical staff and the compliance of patients are improved, and the user interaction unit continuously optimizes the early warning system through a feedback mechanism.

An AI-powered real-time patient risk assessment system (100) for predictive health management, comprising: (a) a data acquisition and integration module configured to collect and standardise patient data from multiple clinical sources, including electronic health records, monitoring devices and laboratory systems; b) a feature extraction and data processing module for pre-processing the collected data and extracting clinically relevant features; (c) a machine learning-based risk prediction module configured to generate real-time risk scores based on the extracted features using trained AI models; (d) a clinical decision support and alerting module configured to trigger alerts and suggest interventions based on risk thresholds; (e) a visualisation and dashboard module for displaying patient risk scores and trends via an interactive interface; and f) a feedback learning and model update module configured to incorporate clinical feedback and retrain the AI models to improve prediction accuracy over time.

The invention discloses a human-in-loop-based medical record data key information extraction and reconstruction method, which is characterized in that a method of introducing a human-in-loop technology into a large language model is adopted, and the large language model and expert priori knowledge are utilized to clean and standardize unstructured medical record data; the semantic retrieval and information understanding ability of the model is enhanced through a medical vector library, efficient extraction of key information is achieved, confidence is generated through self-evaluation, human expert verification is triggered under the condition of low confidence, the model is continuously optimized and updated through expert feedback of a human-in-loop, and the accuracy of human-in-loop verification is improved. And finally, medical record data structured output with higher credibility is realized. Compared with the prior art, the method has the advantages that the accuracy of automatic processing of medical record data is improved, the problem that an automatic processing system is difficult to meet high precision and high credibility is solved, and the method has wide application prospects in clinical decision support, intelligent medical record analysis, personalized diagnosis and treatment scheme making and medical scientific research data mining.

A system and method for clinical decision support include a processor configured to receive a clinical service request; parse the clinical service request into a set of semantic tokens, classify the clinical service request according to the type, severity, and urgency of the clinical service request; execute a preliminary triage operation with the semantic tokens to identify a relevant clinical rule and a primary workflow for responding to the clinical service request based on a classification and / or label, the relevant clinical rule and primary workflow are each unique and tailored for a specific medical service provider organization; generate a decision support recommendation based on the relevant clinical rule and primary workflow; and transfer the decision support recommendation to an organizational processing subsystem for the specific medical service provider organization based on the primary workflow.

Methods, systems, and devices include processing one or more inputs including at least one of patient-provided answers and device utilization information in association with at least one health domain, wherein the at least one health domain is associated with one or more chronic conditions, determining one or more states of a patient in association with the at least one health domain based at least in part on processing the one or more inputs, and storing information describing the one or more states in an electronic data record.

The invention discloses an internal medicine drug curative effect evaluation method based on big data analysis of an electronic health record, and the method comprises the steps: extracting basic health data, diagnosis and treatment time sequence data and drug intervention data from the electronic health record, and carrying out the time-space alignment to generate a dynamic feature set; subgroups are obtained based on disease typing standard hierarchical clustering, and historical data and real world data are fused through transfer learning to construct a subgroup curative effect reference matrix; collecting data after medication in real time, and generating an evaluation vector containing short-term physiological response, middle-term symptom improvement and long-term prognosis risk through deep learning; dynamically matching the evaluation vector with the reference matrix, and introducing an individual weight coefficient to correct deviation; taking the deviation correction value as input, constructing a self-adaptive evaluation model through reinforcement learning, and performing iterative optimization; and generating an individualized report containing the curative effect level, the medication suggestion and the risk early warning, and quantifying the curative effect level through a fuzzy comprehensive evaluation method. According to the method, individual differences are accurately captured, full-cycle dynamic evaluation is realized, and the curative effect evaluation accuracy and the clinical decision-making efficiency are improved.

The invention belongs to the technical field of medical data processing, and discloses an automatic ICD coding method and system based on fusion of various label information, and the method comprises the steps: carrying out the feature extraction of the description of a clinical text and an ICD label, and obtaining the representation of the clinical text and the representation of the label; performing interactive fusion on the ICD tag description and the ICD tag description to obtain tag-related context representation, extracting co-occurrence representation of the tag representation, and fusing the co-occurrence representation with the ICD tag description and the tag-related context representation to obtain enhanced tag representation; performing interactive fusion on the enhanced tag representation and the clinical text representation to obtain a document representation corresponding to each tag, and predicting an ICD code based on the document representation; and defining a loss function, and training the model. According to the method, information from three aspects of context, tag original semantics and tag correlation is fused, richer input features are provided for automatic ICD coding, the comprehensiveness and accuracy of coding are improved, and important support is provided for clinical decision making and medical resource allocation.

Described is a system for receiving a disease specific treatment algorithm corresponding to a disease of a patient from a first Electronic Health Record (EHR) system, processing data corresponding to the disease specific treatment algorithm using a Large Language Model (LLM), receiving one or more CQL models from the LLM based on the processing of the data corresponding to the disease specific treatment algorithm, receiving an update to a. patient record of the patient from the first EHR system, based on the update to the patient record and the one or more CQL models, triggering a Clinical Decision Support (CDS) hook to generate an alert, and causing transmission of the alert for a medical practitioner associated with the first EHR system.

The invention discloses a machine learning-based intra-operative bleeding risk prediction method and device for spinal surgery. The machine learning-based intraoperative bleeding risk prediction method for spinal surgery comprises the following steps: acquiring information of a patient to be predicted; obtaining a trained hemorrhage risk prediction model; and inputting the information of the patient to be predicted into the trained massive hemorrhage risk prediction model so as to obtain a prediction result. According to the method, the high-precision prediction model is trained through large-scale patient data (including basic information, operation parameters, blood indexes and the like), so that the accuracy and the stability of intraoperative SBL risk prediction are improved. And a Cell Saver use suggestion based on a risk threshold is provided, and blood resource allocation is optimized. Clinical decision-making efficiency is improved through an automatic tool, blood transfusion related complications (such as infection and immune response) are reduced, and patient prognosis is improved.

The invention discloses a full-cycle path chronic disease management system and method based on artificial intelligence, and belongs to the technical field of intelligent medical treatment, and the method comprises the steps: generating a model result based on an artificial intelligence chronic disease risk prediction model and a prescription through an active diagnosis and treatment module, and carrying out the active recognition, intervention and management of a target group; the personalized diagnosis and treatment module is used for providing refined follow-up visit, prescription and screening services according to health states and risk characteristics of different individuals; and semantic search, index generality identification and multi-source data integration capabilities of clinical data are provided through an intelligent data governance and decision support module. According to the invention, an intelligent medical new mode of active, personalized and intelligent chronic disease management is constructed, clinical decision is assisted, the management efficiency is improved, the chronic disease management level is improved, reasonable flow of medical resources is promoted, and chronic disease prevention, management and referral are promoted to develop towards the full-life-cycle management direction.

The invention provides an interpretable ML method and system for predicting the death risk of an ICU stroke patient in a hospital, and belongs to the technical field of medical care informatics and electric digital data processing. The ICU cerebral arterial thrombosis patient in-hospital death risk prediction method comprises the steps of S1, inputting variables, related to the in-hospital death risk of the ICU cerebral arterial thrombosis patient, of a current patient; s2, performing in-hospital death risk prediction on the current patient by adopting a pre-constructed optimal prediction model for the in-hospital death risk of the ICU cerebral arterial thrombosis patient to obtain an in-hospital death prediction probability; and S3, performing explanatory analysis on the optimal prediction model by using an SHAP method, and analyzing the importance of variables to a hospital death risk prediction result. According to the method, the hospital death risk of the ICU cerebral arterial thrombosis patient can be quickly and accurately predicted, and an explanatory analysis can be provided for the prediction result, so that a clinical decision is guided.

Systems and methods for performing one or more medical analysis tasks are provided. Patient data of a patient is received for a set of biomarkers acquired at one or more time points within a period of time. The patient data is encoded using an encoder network to generate patient data embeddings. One or more medical analysis tasks are performed based on the patient data embeddings using one or more decoder networks. The one or more medical analysis tasks comprise generating recommendations for a clinical course of action. Results of the one or more medical analysis tasks are output.

The invention belongs to the technical field of medical data processing and health assessment, and discloses a multi-source medical data-based risk prediction method, which comprises the steps of multi-source medical data acquisition, data preprocessing, feature selection, model training, risk prediction and model explanation. According to the method, by adopting a systematic multi-stage feature selection strategy, a specific key feature combination highly related to a specific medical event or disease can be screened out from multi-source heterogeneous medical data including clinical data, laboratory data, heart MRI (Magnetic Resonance Imaging) and the like; the screened feature subsets can be used for constructing an interpretable machine learning model, and through combination with SHAP and other model interpretation technologies, clinicians are helped to understand prediction logic, the credibility of results is enhanced, and more valuable reference information is provided for individualized clinical decision and intervention.