7 results about "Medical decision making" patented technology

This invention relates to the field of medical artificial intelligence technology, and more particularly to a medical decision support method based on training-enhancement separation and bidirectional knowledge assurance. This method divides a medical dataset into basic and enhanced training subsets, trains and optimizes them step-by-step to obtain an enhanced decision model. The model receives the data to be decided and performs reasoning; its candidate results are compared with a medical knowledge base for consistency verification and confidence assessment. Deviations are identified, and adjustment instructions are generated to dynamically correct the model, ultimately outputting the corrected decision result. This method improves the accuracy and reliability of the model's decision-making in complex clinical scenarios.

The present disclosure provides an intelligent medical consultation dialogue method, including: acquiring a medical consultation dialogue from a target user through an intelligent medical consultation dialogue interface; extracting disease feature information from the consultation dialogue; querying a medical decision-making database based on the disease feature information to obtain a suspected disease set; calculating a degree of suspicion corresponding to the suspected disease in the suspected disease set; screening for a target suspected disease from the suspected disease set based on the degree of suspicion; determining whether an unconfirmed disease factor exists in the suspected disease based on the disease feature information; and initiating a question generation task based on the unconfirmed disease factor, performing the question generation task using a large language model to obtain a question targeting the unconfirmed disease factor, and outputting the question. The method provides a transparent and controllable consultation process, reducing risk of diagnostic errors.

An intelligent medical consultation dialogue method, including: acquiring a medical consultation dialogue from a target user through an intelligent medical consultation dialogue interface; extracting disease feature information from the consultation dialogue; querying a medical decision-making database based on the disease feature information to obtain a suspected disease set; calculating a degree of suspicion corresponding to the suspected disease in the suspected disease set; screening for a target suspected disease from the suspected disease set based on the degree of suspicion; determining whether an unconfirmed disease factor exists in the suspected disease based on the disease feature information; and initiating a question generation task based on the unconfirmed disease factor, performing the question generation task using a large language model to obtain a question targeting the unconfirmed disease factor, and outputting the question is provided. The method provides a transparent and controllable consultation process, reducing risk of diagnostic errors.

This invention belongs to the field of medical quality control technology. It discloses a time-sharing quality control system and method for rapid intraoperative pathology diagnosis. By collecting historical time-series data from multiple intraoperative pathology diagnosis processes, a multi-dimensional time benchmark library containing a time window correlation matrix is ​​constructed. Multi-source sensor fusion technology is used to identify node switching events during operation periods. A time-factor coupling network is constructed based on environmental interference parameters and operational coherence parameters. Bottleneck nodes in the diagnostic process time chain are accurately identified, and their cascading delay risk levels are assessed. Finally, a traceable quality control report containing a time topology map is generated. This invention achieves precise time monitoring of the entire intraoperative pathology diagnosis process, scientifically quantifies the contribution intensity of multiple factors to time deviation, improves the timeliness and process stability of pathology diagnosis, reduces surgical waiting time, and provides timely and reliable pathological evidence for medical decision-making.

An AI keystone system for medical decision support generates governed, auditable clinical insights. A medical data governance (MDG) engine ingests heterogeneous patient-related data from clinical systems, medical devices, and clinician inputs, validates and normalizes the data under governance rules, and emits structured, context-constrained representations for identified patients and episodes. One or more intermediary components collect and relay real-time device data so downstream processing operates only on governed data. A reasoning layer with artificial-intelligence models, including at least one large language model, cooperates with modular AI components to process clinician queries and generate candidate clinical outputs. A cryptographic governance subsystem evaluates candidate outputs against patient-safety rules, regulatory mandates, institution-specific policies, and stored consent artefacts using machine-verifiable constraints, enforces dual-factor clinician authentication, and maintains immutable, hash-chained audit trails with trusted timestamps that gate workflow progression. The system enables defensible use of AI-assisted clinical insights across hospital, clinic, and virtual-care environments.

The invention discloses a multi-factor coupling root cause positioning method and system based on a health problem forming path, and relates to the technical field of medical health data processing, and the method comprises the specific steps: obtaining multi-source data through a medical health big data platform, carrying out the standardized integration and alignment, recognizing a health problem, and generating structured information; then modeling by stages to construct a health problem time sequence knowledge graph; anchoring data by using an algorithm and coupling and analyzing influence factors; mining markers, and calculating confidence to generate a root cause set; and finally, outputting a result according to confidence, associating information and linking online and offline intelligent diagnosis and treatment ecology. According to the method, multi-dimensional health data are integrated, a time sequence knowledge graph is constructed, and accurate anchoring of the data is achieved; health factor effects are determined through time sequence coupling analysis, early warning markers are mined, and root cause positioning reliability is improved; and multi-element association and quantitative standards are also established, multiple platforms are connected, accurate reference is provided for medical decision making, and development of a health diagnosis and treatment system is promoted.

The application provides a treatment scheme generation system and method based on iterative generation and feedback, and relates to the technical field of medical decision support, which comprises the following steps: in each iteration process, a planner generates a treatment scheme according to a given patient case and historical information retrieved from a memory, and a evaluator performs multidimensional clinical evaluation on the obtained treatment scheme, and outputs corresponding evaluation scores and evaluation reasons; the treatment scheme generated in each iteration, the corresponding evaluation scores and evaluation reasons are stored in the memory; when a preset stop condition is met, the treatment scheme with the highest evaluation score is selected from the treatment schemes generated in multiple iterations as the final treatment scheme output. The treatment scheme generation system and method based on iterative generation and feedback provided by the application realize accurate, comprehensive and safe generation of treatment schemes by introducing a multidimensional clinical evaluation system, a score-aware memory retrieval mechanism and a dynamic early stopping strategy.