Intelligent monitoring method and system for monitoring the health of a patient in a hospital ward

By collecting multi-source physiological data in real time and utilizing deep learning models and individualized physiological models, the problem of insufficient accuracy in predicting and warning of disease trends in intensive care units has been solved. This has enabled personalized disease prediction and precise nursing care recommendations, improving the management efficiency and patient safety of intensive care units.

CN119763828BActive Publication Date: 2026-04-10THE 971ST HOSPITAL OF THE CHINESE PEOPLES LIBERATION ARMY NAVY
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Patent Information

Application Number
CN202411836107.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-12-13
Publication Date
2026-04-10
Estimated Expiration
2044-12-13

AI Technical Summary

Technical Problem

Existing intensive care unit management methods are not precise enough in predicting and warning of disease trends, and fail to fully consider the multi-dimensional data and individual characteristics of patients, resulting in insufficient accuracy in prediction and warning.

Method used

By collecting multi-source heterogeneous physiological data in real time, using deep learning models to calculate comprehensive physiological indicators, and combining individualized physiological models and dynamic threshold settings, a disease trend prediction vector is generated to achieve accurate disease trend prediction and graded early warning, and to recommend personalized nursing plans.

Benefits of technology

This enables personalized and forward-looking predictions of patients' disease progression, improves the accuracy and feasibility of nursing plans, ensures patients receive the most appropriate care, and enhances the management level of the intensive care unit.

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Abstract

The application relates to the field of biomedical technology, and particularly relates to a digital management method for hierarchical nursing in a monitoring ward based on the Internet of Things. The method comprises the following steps: collecting isomerism data of a patient in real time through a nursing monitoring device to obtain principle physiological data flow; comprehensively calculating physiological indexes of the principle physiological data flow to obtain comprehensive physiological indexes; predicting individual physiological indexes according to the comprehensive physiological indexes, and predicting a probability of illness deterioration of the patient to obtain illness deterioration probability data; predicting a nursing level requirement according to a prediction result of the individual physiological indexes to obtain nursing level requirement probability data; generating an illness trend prediction vector according to the illness deterioration probability data and the nursing level requirement probability data to obtain the illness trend prediction vector. The application can more accurately predict an illness trend and give a hierarchical early warning, thereby better assisting medical staff in hierarchical nursing of the patient and improving the management level of the monitoring ward.
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