A method and system for information management of neurology stroke patients
By constructing a multi-level pathological cascade temporal framework and consistency verification model for stroke ischemia cascade reactions, the problems of stroke patient data fusion and ineffective early warning were solved, enabling accurate disease monitoring and rapid risk identification, thus improving medical safety and efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANXI MEDICAL UNIV
- Filing Date
- 2026-04-30
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies cannot achieve deep integration of multi-dimensional and multi-modal clinical data of stroke patients, making it difficult for medical staff to fully and timely grasp the condition, and ineffective warnings occur frequently, affecting the rapid identification and treatment of critical situations.
A multi-level pathological cascade temporal framework for stroke ischemia cascade response is constructed. Through pathological semantic mapping and time window normalization algorithm, multi-source heterogeneous data are bound to pathological nodes to achieve essential data fusion. Invalid warnings are filtered out through cascade consistency verification model to generate targeted graded warning information.
It achieves data fusion at the pathological level of stroke patients, reduces invalid warnings, improves the speed of identification and timeliness of treatment of critical situations, ensures medical safety, and provides accurate risk assessment basis.
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Figure CN122417451A_ABST