Internet-of-things-based monitoring ward grading nursing digital management method
Through real-time acquisition and deep learning model processing, combined with individualized physiological models and dynamic warning threshold setting, more accurate disease trend prediction and hierarchical warning in the surveillance ward are achieved, improving the accuracy of nursing plans and the closed-loop nature of nursing management.
Patent Information
- Application Number
- CN202411836107.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-13
- Publication Date
- 2025-04-04
- Estimated Expiration
- 2044-12-13
AI Technical Summary
The existing surveillance ward management methods are inaccurate in predicting the disease trend, inaccurate in grading warnings, and lack the integration of multi-dimensional data, which affects the accuracy of the prediction.
By collecting multi-source heterogeneous physiological data in real time, and using deep learning models to calculate comprehensive physiological indicators, we can achieve a comprehensive and accurate quantitative assessment of the patient's physiological status. Combined with individualized physiological models to predict the patient's future physiological indicators, generate a vector of disease trend prediction to achieve personalized and prospective prediction of the patient's disease development trend. The warning threshold is dynamically set based on the disease trend prediction vector and preset rules, and the comprehensive risk score is calculated based on the patient's historical data and risk factor weights to realize an intelligent and hierarchical warning mechanism.
More accurate prediction of the disease trend and hierarchical warning are achieved, improving the accuracy and feasibility of the nursing plan, ensuring that patients receive the most appropriate care, and real-time evaluation and dynamic adjustment of the nursing effect is achieved by continuously tracking the changes in patients' physiological indicators and nursing implementation, and the closed-loop management of the nursing process is achieved.