A postoperative patient care monitoring method, device, equipment and storage medium

By generating feature vectors of patients and using clustering algorithms to identify abnormal states, the shortcomings of manual monitoring in postoperative care are addressed, enabling early risk identification and efficient nursing care.

CN122158192APending Publication Date: 2026-06-05THE FIRST AFFILIATED HOSPITAL OF XIAMEN UNIV
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
THE FIRST AFFILIATED HOSPITAL OF XIAMEN UNIV
Filing Date
2026-02-02
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

The current postoperative care model relies on manual monitoring, which makes it difficult to analyze massive amounts of high-frequency monitoring data in real time and comprehensively. It is easy to miss early signs of complications, and there are also risks of misjudgment and high workload.

Method used

By acquiring postoperative monitoring data from patients, instantaneous and trend values ​​are generated to form feature vectors. Clustering algorithms are then used to identify abnormal states and implement differentiated early warning strategies.

Benefits of technology

It enables early risk identification for postoperative patients, reduces underreporting and false reporting rates, improves nursing efficiency and quality, and optimizes the allocation of medical and nursing resources.

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Abstract

The application provides a postoperative patient care monitoring method, device, equipment and storage medium, and the method comprises the following steps: obtaining postoperative monitoring data of each patient in a target patient group at a current time and postoperative monitoring data of each patient at a past target time; generating an instantaneous value and a trend value corresponding to each patient according to the postoperative monitoring data of each patient at the current time and the postoperative monitoring data of each patient at the past target time, and forming a feature vector according to the instantaneous value and the trend value; clustering each feature vector to obtain a plurality of state clustering clusters; determining an abnormal state clustering cluster from the plurality of state clustering clusters, and executing a differentiated early warning information pushing strategy on the corresponding patient according to the feature vector corresponding to the abnormal state clustering cluster. Through the method in the application, intelligent monitoring of the postoperative patient group can be realized, the abnormal group with potential risks can be located, and hierarchical early warning can be provided, thereby effectively improving the safety and efficiency of postoperative care.
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