58 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.

The invention relates to a flora transplantation clinical pathway standardization management method, device and equipment and a medium. The method comprises the following steps: on the basis of clinical data of a patient and screening data of a donor, executing flora suitability evaluation by using a deep learning model and selecting an adaptive donor; obtaining a flora sample of an adaptive donor, and carrying out high-throughput sequencing; generating a donor flora quality evaluation score according to a preset flora composition diversity index based on a sequencing result; screening the flora samples of which the quality evaluation scores are higher than a preset quality threshold value to prepare a flora preparation; detecting activity parameters and diversity parameters of the flora preparation, and performing colonization potential classification prediction by combining clinical data of the patient and adopting a support vector machine algorithm so as to output a potential colonization success rate; and when the potential colonization success rate exceeds a preset success rate threshold, outputting a clinical transplantation scheme through a preset transplantation scheme generation algorithm based on the flora preparation and the clinical data of the patient. According to the method, through multi-stage intelligent decision making, the safety and the effectiveness are improved.

The invention relates to the technical field of medical informatization, in particular to a clinical aid decision-making optimization method, device and equipment based on guide knowledge, and the method comprises the steps: obtaining electronic medical record data from a hospital information system and a laboratory information management system communication system, data standardization is achieved through the steps of duplicate removal, missing value processing, abnormal value processing, format standardization and the like; according to authoritative knowledge such as clinical pathways, diagnostic guidelines and disease consensus provided by doctors, key feature information is extracted by utilizing a natural language processing technology, structured data is extracted by adopting a regular expression, and unstructured texts are marked by pre-training a medical word vector dictionary, performing word segmentation by a classifier and performing part-of-speech tagging. The weight is optimized based on the loss function; an initial case knowledge base is constructed, and high-quality cases are screened out according to evaluation indexes such as knowledge richness and curative effect; dividing the high-quality cases into well-known physician cases and rare disease cases through syntactic analysis according to the classification indexes; case matching is carried out by adopting selective weighted heterogeneous value distance measurement, discrete attributes adopt value difference measurement, continuous attributes adopt standardized difference square, and attribute weights are optimized by means of a genetic algorithm, so that auxiliary clinical decision and knowledge services are realized. The invention can provide a scheme which can efficiently construct a high-quality case library, automatically extract key feature information, fuse guide knowledge for classification and screening, and introduce more accurate measurement and weight optimization in matching.

The invention relates to a clinical pathway intelligent management system, which comprises a sensing layer, an analysis layer and a decision-making layer, and is characterized in that the sensing layer is connected with medical Internet of Things equipment and is used for collecting vital sign information of a patient through the medical Internet of Things equipment and transmitting the vital sign information to the analysis layer; the analysis layer processes the vital sign information of the patient based on a complication risk prediction model under a graph neural network and a multi-objective optimization algorithm under resource constraints, so as to predict the complication risk of the patient; the decision-making layer comprises a dynamic path generation module and a collaborative decision-making module, the dynamic path generation module is used for displaying the prediction process of different complication risks of the patient, and the collaborative decision-making module is used for a doctor to collaboratively decide the prediction process. According to the invention, the vital sign information can be analyzed according to the personal condition of the patient, so that personalized complication risk prediction can be carried out on the patient. And doctors can participate in co-decision. Vital sign information of a patient can be collected in real time, and therefore body changes of the patient can be analyzed in real time.

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.

The invention discloses an AI service development system based on an agent technology, which relates to the technical field of medical health and comprises a medical professional module, a digital duplication module, a continuous optimization module, an interactive service module, a safety management module, an ethical and compliance management module, a basic service module and an intelligent diagnosis module. According to the AI service development system based on the agent technology, a multi-dimensional medical knowledge graph which comprises a large number of medical entities and relation edges and a dynamically updated clinical path library is constructed by utilizing a semantic network, accurate and reliable medical knowledge support can be provided, and medical data is deeply analyzed in cooperation with an intelligent diagnosis module; the system provides auxiliary diagnosis of complex diseases, improves the accuracy and efficiency of diagnosis, is high in intelligent level, and can provide accurate and efficient diagnosis service for patients especially in the aspect of diagnosis and treatment decision support of the complex diseases.

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 invention provides an intelligent decision support method and device for clinical pathway construction. The method comprises the following steps: acquiring a computed tomography image and a metabolism image; integrating the tumor morphological data, the volume data and the metabolic activity data to generate a radiomics tag; performing microvascular tomography on the target surgical puncture path to generate microvascular density distribution data; performing tumor subregion segmentation processing on the computed tomography image to obtain tumor subregion data, and generating sensitivity distribution data based on the data; identifying a high-risk blood vessel region, generating a roundabout trajectory in the target surgical puncture path, and accordingly obtaining an optimized path scheme and a postoperative medication sequence scheme; and cooperatively integrating and processing the optimized path scheme and the postoperative medication sequence scheme, and generating clinical path decision data. According to the technical scheme provided by the invention, the operation path and the medication time sequence are collaboratively optimized through the multi-source medical data, and intelligent decision-making of the clinical path of tumor interventional therapy is realized.

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 invention provides a diagnosis and treatment path item intelligent prediction system and method based on multi-modal deep learning and an attention mechanism, and belongs to the field of medical information technology and artificial intelligence. A hierarchical attention mechanism is introduced, the incidence relation among diagnosis, operation and cost is automatically learned, and combined prediction of the occurrence probability and the use number of diagnosis and treatment items is achieved by designing a double-task collaborative learning framework; dynamically adjusting the cost weight according to the severity of illness, complications, complication conditions and treatment progress by adopting an attention mechanism perceived by the state of a patient; in combination with a probabilistic prediction framework, a confidence coefficient and an adjustable recommendation list are generated for each cost item, so that real-time updating and differentiated cost suggestions are supported, and individualized cost management is met; the method can be applied to scenes such as clinical pathway making, hospital cost control and medical fine management.

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 invention provides a clinical pathway and DRG cost collaborative management and control method and system. The method comprises the following steps: acquiring multi-source heterogeneous medical data; constructing a medical knowledge graph based on the medical data; reasoning the initial diagnosis and treatment information of the patient based on the medical knowledge map, and predicting and outputting a recommended clinical path of the patient and a corresponding DRG pre-grouping result; monitoring the deviation between the actual diagnosis and treatment behavior of the patient in the diagnosis and treatment process and the recommended clinical path and the difference between the actual cost and the DRG pre-grouping cost in real time, and performing intervention when a preset condition is triggered; and collecting complete diagnosis and treatment process data and a path execution result after the patient is discharged from the hospital, and feeding back an analysis and evaluation result to the medical knowledge graph after analysis and evaluation so as to carry out iterative optimization on the knowledge graph. According to the application, the originally isolated clinical pathways, DRG grouping, patient diagnosis and treatment and cost information are deeply fused and associated, and a rigid and static management mode of the traditional clinical pathways is broken through.

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 application discloses a clinical pathway curative effect index data scientific research achievement retrieval and archiving method and system, and belongs to the field of clinical medical information management. A clinical pathway template containing multiple nodes and node collection rules is acquired; curative effect index data collected at each node and scientific research achievements generated based on the data are acquired; a curative effect index name recorded in the scientific research achievements is compared with a should-collect index name recorded in the node collection rules to determine a belonging node; a correlation file is established to record a mapping relationship among the node, the curative effect index data, the scientific research achievements, the belonging node and the curative effect index data; a retrieval condition is received, and matched scientific research achievements are found based on the correlation file, and a retrieval and archiving result is output. The application realizes accurate correlation and archiving of scientific research achievements and clinical pathway nodes, supports multi-dimensional efficient retrieval, and improves the accuracy and result reliability of retrieval and archiving.

The application provides a gynecological clinical path prediction method based on neuron level feature selection, and belongs to the technical field of path prediction, and comprises the following steps: dividing gynecological electronic medical record data of a patient into time sequence features and baseline features, inputting the time sequence features into a trained GRU network for feature coding, obtaining dynamic change rules of various clinical indexes over time, and converting the baseline features into feature vectors through an embedding layer; calculating feature importance based on the patient's last gynecological visit record and the visit scene, projecting the dynamic change rules of various clinical indexes over time and the feature vectors into a new representation space based on the feature importance to obtain a projection matrix to generate a gating vector, and screening and fusing the features of a main task layer and an auxiliary task layer respectively to obtain a final feature representation and obtain a multidimensional prediction result. More personalized and accurate diagnosis and treatment suggestions are provided for doctors.

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.