6 results about "Clinical state" patented technology

This application provides a multi-agent, multimodal collaborative method, device, equipment, and storage medium for diagnosing the condition of patients in the intensive care unit (ICU), belonging to the field of medical artificial intelligence and intelligent analysis technology for intensive care. The method includes: receiving multimodal clinical monitoring data in an ICU setting, including bedside medical images, continuous life time series, and critical care clinical text; identifying and adaptively routing the input data through a modality detection agent, distributing it to corresponding domain expert agents for pathological and physiological feature extraction; constructing a patient-centric graph structure representing the dynamic clinical condition of ICU patients through a knowledge graph agent based on the features output by each domain expert agent, this graph structure integrating structured medical knowledge of entity extraction and relational reasoning; further, inputting the constructed graph structure and original multimodal features into a collaborative agent, generating the final ICU patient condition diagnosis result (such as mortality prediction, ICU long-stay prediction, etc.) and a traceable reasoning path through graph traversal and collaborative reasoning. This application effectively breaks down data silos between different monitoring devices in the intensive care unit environment, dynamically captures cross-modal associations specific to critically ill patients, and significantly improves the accuracy of critical care clinical diagnosis and medical trust.

This invention discloses a digital intelligent medical rehabilitation diagnosis method and system based on closed-loop management, comprising the following steps: analyzing data using hierarchical reasoning and interpretable generation mechanisms to provide relevant suggestions, mapping these suggestions to training action templates; dynamically calculating training intensity based on the patient's current functional state, and dynamically assembling prescription parameters based on the training intensity using an incremental prescription update mechanism to update the prescription; updating the prescription and follow-up plan based on a dynamic strategy of clinical status. This invention supports dynamically assembling training intensity and frequency according to the patient's real-time status, forming truly personalized electronic prescriptions; finally, through prescription versioning management, intelligent follow-up node planning, and an automatic re-evaluation triggering mechanism, a data-driven closed loop covering the entire rehabilitation cycle is constructed, ensuring that the intervention plan continuously evolves with the patient's recovery process.

This application discloses a digital management method and system for hospital nursing quality indicators, relating to the field of medical and nursing quality information technology. The method includes the following steps: setting assessment rules for evaluating nursing process quality indicators; acquiring patient nursing records and constructing a patient-centered event time series; based on the assessment rules, filtering nursing events related to the corresponding nursing process from the event time series; dynamically adjusting the strictness of the assessment rules based on the identity information of nursing staff and the clinical status information of patients; verifying the event time series according to the adjusted assessment rules and obtaining verification results; determining the compliance of the nursing process based on the verification results, and generating a compliance conclusion and audit report for the nursing process. This application can intelligently distinguish between genuine nursing quality defects and discrepancies in record appearances caused by differences in personnel and context, solving the problems of fragmented processes, isolated data, and delayed assessment in traditional nursing quality management.

This invention discloses a multidisciplinary pre-consultation method and system based on multi-agent collaboration. The method includes: 1) Clinical state initialization: constructing a structured clinical state and dividing it into a case feature information set and a diagnosis and planning set; 2) State-driven agent scheduling: scheduling one or more specialist agents related to the current symptoms based on the current clinical state and historical dialogue information; 3) Parallel analysis and suggestion generation: each scheduled specialist agent independently analyzes the current state and outputs problem suggestions and state update suggestions; 4) Clinical state update: semantically aggregating the outputs of multiple agents, updating the case feature information set, and generating the next round of patient-facing inquiries; 5) Diagnosis generation: when the case feature information set meets the overall integrity condition, generating a diagnostic conclusion and treatment plan based on the set, forming the initial medical record. This invention improves the structuring level, diagnostic accuracy, and clinical interpretability of intelligent consultation without requiring real multidisciplinary consultation resources, and has broad application prospects.

The application discloses a kind of based on knowledge graph's electronic medical record accurate search method and system, comprising the following steps: obtaining data and preprocessing, form clinical observation time series data sequence;Based on clinical observation time series data sequence constructs medical knowledge graph and generates medical knowledge control path;Continuous time clinical observation control path is constructed, and double control path input structure is formed;Improved neural controlled differential equation is input, and initial clinical state continuous trajectory is obtained;Medical reachable domain determination and reachable domain projection operation are executed, and target clinical state continuous trajectory is obtained;According to search request, reverse continuous time integral calculation is executed, and candidate historical clinical state continuous trajectory set is obtained;The stability evaluation value of trajectory is calculated;According to trajectory stability evaluation value, sorting is carried out and electronic medical record search result is output.The application combines knowledge graph and neural controlled differential equation, realizes the time continuity and stability of electronic medical record accurate search.

The present application belongs to the field of patent application, and relates to an infection risk prediction method caused by gastrointestinal bleeding, comprising the following steps: collecting multi-source clinical data of patients with acute gastrointestinal bleeding, and constructing an infection risk feature set; constructing an initial state diagram, and determining the node state of the graph node in the initial state diagram and the activated graph node and graph edge based on the infection risk feature set to obtain a clinical state diagram; extracting the path of the graph node and the graph edge in the clinical state diagram to obtain an infection transmission path set; quantifying the risk of each infection transmission path based on the node state to obtain the path risk value of the infection transmission path; and performing aggregate calculation on the multiple path risk values of the infection transmission path set to obtain the total infection risk; the risk prediction can not only give a probability result, but also explain the risk source, thereby significantly improving the practical value of the clinical decision support system.