40 results about "Clinical pathway" patented technology

The invention discloses a knowledge graph-based cerebral hemorrhage clinical pathway intelligent recommendation method and system, and the method comprises the steps: constructing a pathway knowledge graph and an illness state grading system through analyzing a diagnosis and treatment guide, recognizing a key node of pathway execution and an illness state grade switching boundary, and configuring a differentiation decision rule for a single illness state region and a mixed illness state region; the method comprises the following steps of: performing dimensionalization acquisition on multi-source illness state data of a patient, extracting illness state characteristics, monitoring a dynamic index change trend, generating illness state description through weighted characteristic extraction and dynamic early warning analysis, and calculating a matching degree between a current illness state and a candidate path; when path execution deviation is detected, a deviation source node is positioned through cross-stage transitivity analysis and reverse tracing, and a targeted path correction instruction is generated; normativity of an adjustment scheme is ensured through node integrity verification and forced constraint correction, a clinical pathway recommendation result matched with the actual illness state of the patient is output, and adaptability and accuracy of clinical pathway recommendation are improved.

The invention belongs to the technical field of artificial intelligence and medical information processing, and particularly relates to a medical text big data mining system based on natural language processing, which comprises a medical field pre-training module, a text structured extraction engine module, a knowledge graph guide reasoning module, a clinical path optimization module and a medical question and answer and literature mining platform module, according to the system, a self-adaptive hierarchical attention mechanism is adopted, and medical texts of any length are processed through dynamic fusion of local attention and global attention; constructing an entity dependency relationship graph and coding semantic association between entities by adopting a graph neural network; introducing a medical ontology knowledge graph to carry out conditional random field joint reasoning, and correcting unreasonable annotations; the optimal diagnosis and treatment mode is mined and recognized through the frequent mode, the entity recognition F1 value reaches 96.2%, the long-distance relation recognition accuracy reaches 91%, the structured data accuracy reaches 93.7%, the data preparation time is shortened by 85%, and the document review efficiency is improved by 320%.

The present disclosure introduces an AI-based system (100) and method for predicting the risk of acute exacerbation of chronic obstructive pulmonary disease (COPD) over three months. The system interfaces with consumer devices through REST APIs, ensuring secure communication via HTTPS. Client modules collect and pre-process raw data for integrity and compatibility. This data is then sent to the server (104) which is then transmitted to Input module, the input module (112) authenticates and validates it. The module processes personal details, respiratory history, allergies, vaccination and medication history, and demographic data for air quality analysis. The prediction module (116) uses a CART Model with 90% accuracy to evaluate COPD risk. Based on predefined thresholds, the clinical pathway module (118) and risk prediction response module (120) offer personalized treatment protocols and clinical decision support. The system complies with ISO 13485 standards for safety and reliability.

The invention discloses a digital management system for the health state of a patient after an anorectal operation, and relates to the technical field of digital management of the health state after the operation. Comprising a data acquisition and processing module which is used for calling an anorectal postoperative clinical path rule engine based on real-time symptom data input by a patient and the number of postoperative days, and dynamically generating and pushing a corresponding structured interactive data acquisition sequence; and the feature extraction module is used for carrying out real-time feature extraction on a postoperative recovery related image provided by a patient through a preset image analysis model at a patient terminal side so as to generate a multi-modal structured data set containing time sequence physiological indexes and image feature vectors. According to the method, clinical priori knowledge is fused through the multi-modal neural network, a compound and atypical early complication mode is deeply recognized from time sequence physiological indexes and image features, the early discovery rate of complication is increased, and the false alarm rate caused by conventional recovery variation is reduced through explainable risk contribution factor output.

This invention relates to the field of medical intelligent technology and discloses a method and system for intelligent recommendation of laboratory tests based on patient diagnosis and treatment information. The method includes: extracting clinical events from time-series diagnosis and treatment information to obtain standardized clinical events; determining the medical logical relationships of the standardized clinical events and labeling causal, temporal, and co-occurrence relationships to obtain labeled clinical events; constructing a disease evolution network with labeled clinical events as nodes and medical logical relationships as edges; analyzing the topology of the disease evolution network to obtain critical paths and hub nodes, and resolving the hub nodes as core clinical diagnosis and treatment intentions; associating and matching the core clinical diagnosis and treatment intentions with a pre-constructed dynamic correlation graph of test-intentions to obtain test item combinations; and ranking the test item combinations according to the clinical path fit based on the current diagnosis and treatment stage and individual characteristics to obtain a personalized test item recommendation list. This invention can improve the efficiency of test item recommendation.

This invention relates to the field of medical information technology, and in particular to a method and system for generating clinical multi-agent task sequences. The method includes the following steps: acquiring and structuring core clinical objectives, which include patient basic information, clinical indicators, and disease management goals; decomposing the core clinical objectives into several structured sub-objectives with dependencies and execution order based on a clinical pathway template library and a medical knowledge graph; for each structured sub-objective, splitting it into multiple minimum executable clinical sub-tasks according to a clinical sub-task decomposition rule library, and defining the execution subject type, input / output, and dependencies for each clinical sub-task to construct a sub-task-level directed acyclic graph; integrating the sub-task-level directed acyclic graphs, identifying dependencies across structured sub-objectives, generating a global clinical task directed acyclic graph, and performing dependency verification and clinical logic consistency verification.

The invention belongs to the technical field of diabetic foot clinical path management, and discloses a diabetic foot patient path management method and system based on lower limb vasculopathy grading. Mapping the ulcer position to the corresponding perfusion area and the artery segment to generate ulcer space characteristics; calculating a blood supply risk score of the perfusion area and generating a space risk map by combining the blood vessel image detection data and the microcirculation detection data; then collecting time sequence data including an ulcer index, an infection index, a blood supply index and a space risk map, and predicting the progress speed and the risk level of the future ulcer course; and finally, according to the blood supply risk score and the disease course prediction result, generating an individualized path management scheme through a joint triggering rule. According to the invention, precise and dynamic management of the diabetic foot ulcer is realized, the healing rate is improved, and the recurrence rate and the medical cost are reduced.

The invention discloses an optimization method and device for medical large model auxiliary diagnosis and treatment and a computer program product, and the method comprises the steps: firstly carrying out the term standardization of target case information of a target patient, and keywords in a preliminary diagnosis and treatment scheme outputted by an LLM; after TF-IDF vectors and dense semantic vectors of a target keyword set composed of the keywords and the standard medical terms of the keywords are generated, fusion processing is carried out on the TF-IDF vectors and the dense semantic vectors, and mixed vectors are obtained; a clinical path knowledge graph conforming to personalized features of a target patient is dynamically constructed by using more accurate candidate knowledge texts retrieved from a medical knowledge base, and a preliminary diagnosis and treatment scheme output by a medical LLM is continuously and intelligently corrected and detected medical contradictory points are adjusted in combination with a portrait of the target patient, so that the clinical path knowledge graph is obtained. And finally, obtaining a final optimized diagnosis and treatment scheme. Therefore, closed-loop correction optimization of medical LLM auxiliary diagnosis and treatment results is realized, and an ideal optimization effect is achieved.

The application relates to the technical field of sales support, and discloses a medicine sales data analysis method, which comprises the following steps: constructing a cognitive map model; performing clinical path complexity grading; applying a clinical path complexity grading algorithm to analyze clinical decision process data of a medical institution, and calculating a clinical path complexity index; implementing cognitive-clinical path matching; adopting a cognitive-clinical path matching algorithm to process sales representative cognitive state data and clinical path complexity data, and calculating a matching degree score; performing hierarchical progressive information presentation; based on a hierarchical progressive information presentation algorithm, processing the matching degree score and the clinical path data, dividing information into different priority levels, and outputting an optimized information presentation sequence; and providing personalized clinical knowledge navigation; the application significantly improves the accuracy and effectiveness of professional information transmission by intelligently adapting the cognitive ability of a sales representative to the complexity of a clinical path.

The application relates to a method and system for mining and analyzing the correlation of clinical comorbidity of discharged patients. The method comprises the following steps: constructing an individual diagnosis and treatment narrative graph for each patient according to the discharge medical record text, generating a time sequence transaction sequence corresponding to each patient, and constructing a sequence database; based on the database, the original time sequence frequent pattern set is constructed by analyzing through an improved generalized sequence pattern algorithm; each pattern in the set is classified to obtain multiple classification clusters, and the original time sequence frequent pattern in each classification cluster is processed through multi-sequence alignment to construct a generalized clinical path graph, and the information in the generalized clinical path graph is extracted to generate a natural language abstract. The method improves the time sequence logic, knowledge abstraction degree and clinical interpretability of the clinical comorbidity correlation mining by constructing a diagnosis and treatment narrative graph, mining a time sequence frequent pattern, and constructing a generalized clinical path graph through clustering and multi-sequence alignment.

An AI-based system (100) designed to assess prediabetes risk in individuals. The system (100) integrates seamlessly with the user devices via rest APIs, ensuring secure communication through https. A data collection module (122) collects raw data forwards the data to standardization and pre-processing modules (124) for preparation. The pre-processed data is transmitted to the server (104) through a network (102) using an API integration module (126) and thereafter forwarded to the input module (112) that authenticates and validates patient data, which is transmitted to the processing module (114) that refines the data, while a prediction module (116) employs a deep learning model with an AUC of 0.87 to evaluate prediabetes risk. A clinical pathway (118) and risk prediction response (120) modules provide personalized protocols, risk status, and clinical recommendations. It (100) complements clinician decision-making, adheres to ISO 13485 standards, and serves as a cost-effective community-based prediabetes risk assessment.

This invention discloses a method, medium, and device for identifying DRG over-compensation based on invoice reverse engineering and causal imprinting. The method includes: acquiring detailed medical billing data from the medical records to be audited; extracting features of treatment items and mapping them to a medical knowledge graph to obtain a set of feature atoms; inputting the feature atom set into a large language model, outputting real disease phenotypes as pseudo-labels, and performing semantic comparison with the reported primary diagnosis to identify over-compensation of the primary diagnosis; classifying the sensitivity of other reported diagnoses by score, screening high-value complication entities, and verifying their resource consumption matching degree through a clinical pathway constraint library to identify fictitious complications; extracting the main billing items and their low-value billing imprint rule library, and identifying falsely inflated primary intervention items through counterfactual causal comparison; finally, calculating a comprehensive anomaly index based on deviation, matching degree, and divergence, and outputting an audit report. This invention automatically identifies DRG over-compensation anomalies from multiple dimensions, improving the accuracy and comprehensiveness of audits.

The invention discloses a fine-grained medical data link up-link and down-link cooperative interaction method and system, and is applied to a special disease clinical pathway supervision scene. The system comprises a plurality of medical institution nodes, each node is registered and added into a Fabric block chain to jointly maintain an abstract chain, and an inquiry module, an on-chain and off-chain storage module, an intelligent contract module, a supervision module, a shared fine granularity module and an on-chain and off-chain combined efficient inquiry and auditing module are deployed; the inquiry module is used for forming medical data generated during patient inquiry; the shared fine granularity module is used for realizing a shared fine granularity mechanism; the on-chain and off-chain storage module is used for storing data under the chain; the intelligent contract module is used for uploading and downloading the abstract; the on-chain and off-chain combined efficient query and audit module is used for selecting a proper shared fine-grained set and a query table according to a request; the auditing sub-module is used for auditing the data, and the supervision module supports a special disease clinical pathway supervision function and other safety operations.

This invention relates to the field of data analysis technology, specifically disclosing a method and system for secure sharing and exchange of ICU data. The method and system are characterized by the following steps: Patient status data is input into a pre-built clinical pathway prediction model to generate predicted pathway data; based on key department identifiers in the predicted pathway data, a pre-authorization token is generated for each key department identifier, and the generated pre-authorization token is sent to the corresponding department's server; according to the pre-authorized data range identifier, matching target patient data is filtered from the patient status data, and the target patient data is pre-fetched to the edge cache node corresponding to the department server; access intent data is extracted from data access requests; it is determined whether there is a target pre-authorization token that matches the department identifier and is in a valid state; and it is determined whether the data type identifier of the requested access falls within the pre-authorized data range identifier associated with the target pre-authorization token. The advantage is that it improves the efficiency of secure data sharing and exchange.

The application relates to a clinical-pathway-based intelligent medical consumable consumption control system and method, wherein the system comprises: a consumption control management data layer, which is used for performing heterogeneous data fusion between a hospital information system and a supply chain system through a standardized interface engine; a consumption control management rule layer, which is used for warning and controlling over-packaging behaviors of overuse of medical consumables on a clinical pathway from the quantity dimension and the amount dimension; and a consumption control management application layer, which is used for visually displaying charging item information of the over-packaging behaviors in the case of identifying the over-packaging behaviors. Through the application, fine control of medical resource consumption is realized, the architecture takes the clinical pathway as a benchmark, deeply couples medical behaviors and material consumption, innovatively constructs double-dimension dynamic control of quantity+amount, effectively avoids resource waste or insufficient supply caused by traditional single quantity limitation, and solves the problem of how to improve fine allocation of medical resources.

The application provides a kind of fusion voiceprint feature dental case generation method and system, through high sensitivity multi-channel pickup and edge real-time processing, realize multi-device acoustic fingerprint extraction, and based on spectrum analysis, identify device mechanical state transition;Combined with natural language processing and standard medical terminology mapping, the operation log of medical record is standardized coded, and is normalized to clinical path logic;Adopt cross-modal graph attention mechanism to realize the accurate space-time synchronization of voiceprint event and medical record event, then encrypt the corrected data binding, generate unique, anti-fake fusion data, finally through block chain distributed evidence guarantee non-tamperable, the application significantly improves diagnosis and treatment data synchronization, credible evidence and identity anti-fake ability.

The present application relates to an executable clinical path generation method, device, electronic equipment and storage medium, the method comprising: obtaining original medical text, and calling a large model to expand the content of the original medical text to obtain expanded medical text meeting a preset expansion rule; inputting the expanded medical text into a fine-tuned open source base model to call the fine-tuned open source base model to generate a semi-structured template based on the expanded medical text; sending the semi-structured template to a front end for visual display to verify whether the semi-structured template meets a preset structure rule; and when the semi-structured template meets the preset structure rule, converting the semi-structured template into a GoRules rule and generating a structured clinical path to be executed according to the GoRules rule. The present application not only guarantees the accuracy and stability of medical execution statement generation, but also reduces the fine-tuning period of the large model to a certain extent.

The present disclosure introduces an AI-based empirical antibiotic recommendation system (100) designed to analyse patient data and recommend antibiotic treatments. The system (100) integrates with user devices via REST APIs and ensures secure operation through HTTPS. Client modules collect and pre-process raw data, ensuring integrity and compatibility. The pre-processed data is sent to the server (104) and forwarded to an input module (112) for authentication and validation. A processing module (114) standardizes the data, while a prediction module (116) identifies probable organisms and their antibiotic sensitivities. A clinical pathway module (118) generates dosing guidelines, and a prediction response module (120) displays the top three organisms with associated probabilities and recommended antibiotics. The antibiotics are color-coded for safety, and a feedback mechanism allows continuous system refinement.

The present disclosure proposes an adult early warning system (EWS) (100) for predicting clinical deterioration and mortality risks in patients. The EWS (100) integrates via API, utilizing REST API and HTTPS for secure communication. It features an input module (112) for authenticating and validating data, which is transmitted through a network (102) to a server (104). The server (104) processes data through data collection, standardization, and pre-processing modules (122, 124) before it is analyzed by the prediction module (116). Results are aligned with the clinical pathway module (118) and presented by the risk prediction response and patient overview dashboard module (120), which provides risk scores, and graphical representations of vitals, lab results, and ECG data. The EWS (100) supports clinicians by enhancing decision-making and adhering to ISO standards for patient safety and reliability.

The present application relates to the technical field of health care informatics, and particularly relates to a health data collection and analysis platform for chronic disease management of the elderly, which comprises a semantic perception edge node, a space-time alignment transmission middleware, a causal reasoning analysis engine and a clinically interpretable intervention generation layer, and realizes end-to-end closed-loop optimization through sensing end context perception, transmission layer dynamic semantic encapsulation, analysis layer state transition graph neural network modeling, and output layer clinical path driven intervention instruction generation. The present application significantly improves intervention accuracy and timeliness, enhances user compliance and reduces system power consumption.

The present invention relates to method of managing clinical pathways for a patient admitted to hospital, comprising measuring the level of nucleosomes present in a body fluid sample obtained from the patient upon admission to the hospital; and determining the health care actions for the patient based on the level of nucleosomes measured in the sample.

The application provides a clinical path variation detection method and device and electronic equipment, and relates to the technical field of medical services, which alleviates the technical problem of low detection accuracy of clinical path variation in the prior art. The method comprises the following steps: obtaining a historical medical order sample; based on the historical medical order sample, an execution rule library corresponding to the historical medical order sample is established; obtaining to-be-detected medical order data; whether the to-be-detected medical order data has a clinical path variation is detected through the execution rule library, and a detection result is obtained.

The application discloses a clinical path deviation risk management and control tracing method and device based on big data and AI, equipment and a medium, comprising: preprocessing multi-source heterogeneous clinical data; dividing a time sequence stage based on the preprocessed multi-source heterogeneous clinical data, and constructing a stage-specific risk feature library; constructing a time sequence causal forest model based on the stage-specific risk feature library, and combining a real-time risk cross-stage prediction mechanism to construct a double-level clinical path deviation risk early warning model; constructing a full-link causal tracing graph based on a structural causal model, and performing hierarchical positioning and tracing of the main cause of deviation; and constructing a personalized correction scheme generation model based on the results of the double-level clinical path deviation risk early warning model, the full-link causal tracing graph and the hierarchical positioning and tracing. The application belongs to the field of medical risk prediction. The application can ensure the continuity of the treatment decision scheme.

This invention discloses an intelligent recommendation method and system for clinical pathways in cerebral hemorrhage based on knowledge graphs. It constructs a pathway knowledge graph and a disease severity classification system by analyzing treatment guidelines, identifying key nodes in pathway execution and disease severity level switching boundaries, and configuring differentiated decision rules for single and mixed disease severity areas. It collects multi-source disease data from patients in a dimensional manner, extracts disease features, and monitors dynamic indicator trends. Through weighted feature extraction and dynamic early warning analysis, it generates disease descriptions and calculates the matching degree between the current disease severity and candidate pathways. When a deviation in pathway execution is detected, it locates the source node of the deviation through cross-stage transitivity analysis and reverse tracing, generating targeted pathway correction instructions. Through node integrity verification and mandatory constraint correction, it ensures the standardization of the adjustment plan, outputting clinical pathway recommendation results adapted to the patient's actual condition, thus improving the adaptability and accuracy of clinical pathway recommendations.

The invention belongs to the technical field of extra-specification medication management, and discloses an intelligent system and method for extra-specification medication management, and the system comprises an extra-specification medication database, the extra-specification medication database is provided with an interaction system, the interaction system recognizes prescription medical advices issued by his, and the extra-specification medication database is internally provided with a judgment system. The judgment system is used for judging whether the out-of-specification medication database has the prescription medical advice issued by his or not; according to the method, diagnosis and treatment specifications, clinical diagnosis and treatment guidelines and foreign specifications published by expert consensus, international authoritative association or organization, diagnosis and treatment specifications, clinical diagnosis and treatment guidelines, guidance principles and clinical pathways published by national health committee, systematic evaluation of RCT or Meta analysis literatures, and clinical researches of random contrast are combined into a whole. And special textbooks, case contrast and queue research are imported into a database, rules are set in a specification-exceeding medication database, and corresponding evidence-based bases are automatically popped up to doctors.

The application discloses an artificial intelligence-based general disease intelligent preliminary screening diagnosis system, relates to the technical field of medical artificial intelligence, constructs a "data-knowledge-reasoning" closed loop, integrates medical data such as medical records of third-grade class-A hospitals, public health health records and diagnosis and treatment guidelines, extracts key information through natural language processing, converts a clinical path into a structured "disease-feature" correlation library containing symptom intensity coding, and realizes intelligent preliminary screening. The system sets a "medical consultation guidance + result output" module for primary doctors, prompts the collection of chief complaints and generates a disease list with high, medium and low possibility levels and clinical basis; the system sets a "symptom + communication interpretation" module for patients, and outputs diseases, treatment suggestions and typical performance illustrations. The diagnosis suggestion is attached to a traceable reasoning chain, and the system is internally provided with a regional disease library and equipment adaptation rules, and is suitable for the actual situation of primary medical institutions. The system supports dynamic iterative updating of the knowledge base, can be connected to a primary HIS system, realizes two-way calling of health records, and supports continuous diagnosis and treatment.

The invention relates to the technical field of image recognition, and particularly discloses a clinical pathway image recognition method and system based on a large model, and the method comprises the steps: segmenting a clinical pathway image through adaptive noise reduction, edge enhancement and a U-Net + + model, and obtaining a text region, a symbol region and a background region; extracting multi-modal features and spatial association through a medical special OCR model and a convolutional neural network; reasoning an entity logic relationship through a multi-mode large model finely adjusted in the medical field in combination with a clinical path knowledge graph; and finally, converting into a standardized JSON format, and carrying out dynamic verification and secondary correction through a clinical rule engine. According to the method, the accuracy and the structuring degree of clinical pathway image recognition are improved, image type clinical pathway data are efficiently converted, reliable data support is provided for large-model clinical application, and medical diagnosis and treatment specification requirements are met.

This invention discloses a method for evaluating the consistency of decision-making behavior in a multimodal medical AI system, belonging to the field of medical artificial intelligence evaluation technology. The method first constructs a multimodal clinical pathway knowledge graph integrating images, text, and standard knowledge as an evaluation benchmark, and parses the decision output of the AI ​​to be evaluated into a structured directed graph. Then, it utilizes a cross-modal embedding model to unify the semantic representation of nodes, and through a cross-modal hypergraph neural network, comprehensively calculates the consistency across the structural, semantic, and modal dimensions. An interpretable consistency score is generated based on a dynamic weighted graph edit distance algorithm, and anomalies are automatically detected. Furthermore, this method supports interactive dynamic testing and robustness analysis. This invention achieves automated, comprehensive, and interpretable consistency evaluation of the medical AI decision-making process.

The application discloses a medical self-service machine intelligent triage method, a storage medium and equipment, first, a differential diagnosis set is generated based on symptom description information, personal health record information and a real-time epidemiology prior probability model, and a clinical path time-prognosis model is used to perform Monte Carlo simulation on each candidate path to form a risk-reward quantitative portrait; combined with personal preference information and real-time resource state data of the hospital, a multi-objective optimization algorithm is used to calculate a personalized recommendation score, and an optimal candidate path is screened; if the department capacity of the hospital is over the limit, a regional medical resource collaborative query based on a consortium chain smart contract is triggered to generate a shunt triage scheme, and finally, a structured hierarchical triage path graph is output, and a closed-loop service of online review and offline appointment is started. The application realizes accurate triage from quantitative diagnosis, prognosis evaluation to cross-institution resource collaboration, and significantly improves the scientificity, individualization level of triage decision and overall utilization efficiency of regional medical resources.

This invention relates to a CKD clinical pathway recommendation method based on temporal dynamic graph clustering and multi-task joint optimization, belonging to the field of medical big data analysis and smart healthcare technology. The method includes the following steps: acquiring multi-source clinical data; introducing an exponential decay function conforming to the forgetting curve to calculate temporal weights; constructing a clinical diagnosis and treatment interaction graph with time attributes; constructing a two-stream parallel neural network; extracting the static attribute embeddings and dynamic structure embeddings containing temporal dimensions of nodes and performing weighted fusion; subsequently constructing a joint loss function including the main task of recommendation ranking, macro-clustering, and micro-alignment auxiliary tasks; and performing multi-task end-to-end joint optimization of the model; finally, calculating the node embedding scores and selecting Top-K to generate a clinical pathway recommendation list. This invention effectively filters outdated pathological feature noise and significantly improves the accuracy and robustness of the auxiliary diagnosis and treatment system under sparse medical data.