Ward call-for-help method and system based on smart medical treatment

Through multimodal data fusion and advanced mathematical tools, a smart medical ward call-up system is built, which solves the shortcomings of the existing system, realizes a comprehensive and accurate assessment and timely response to the patient's status, and improves the accuracy and prediction ability, adaptability and reliability of call-ups.

CN120452790APending Publication Date: 2025-08-08SICHUAN BOYA ZHIXIN TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202510573898.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-06
Publication Date
2025-08-08

AI Technical Summary

Technical Problem

The existing ward call system cannot fully reflect the patient's true status, and it has long response time, false or missed reports, lack of prediction capabilities, and insufficient stability and reliability in a complex and changeable medical environment.

Method used

Multimodal data fusion technology is adopted, combining random matrix transformation, topological feature mapping, group theory-based risk assessment and chaotic dynamic system, and a smart medical ward call-up system is built. By obtaining multiple sensor data, data fusion, feature extraction, abnormality detection and risk assessment are carried out, and early warning index is calculated to achieve accurate call-up judgment and response.

Benefits of technology

It improves the accuracy and timeliness of call for help, reduces false alarm rates and missed response rates, has predictive capabilities, adapts to different patients and environments, enhances the robustness and interpretability of the system, and provides personalized monitoring solutions.

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Abstract

The invention relates to the technical field of medical informatization, in particular to a ward distress call method and system based on intelligent medical treatment, and the method comprises the steps: obtaining patient state data collected by a plurality of sensors in a ward; receiving a ward help-calling instruction; constructing a multi-modal data fusion matrix based on the patient state data; executing random matrix transformation and feature extraction according to the multi-modal data fusion matrix; based on the extracted features, topological feature mapping and anomaly detection are carried out; executing group theory base risk assessment according to the topological features; calculating a chaotic power system early warning index based on a group theory risk assessment result; determining a call-for-help judgment result according to the early warning index; a corresponding response strategy is executed based on the call-for-help judgment result, the system can comprehensively grasp the state of the patient through the multi-modal data fusion technology, and the evaluation accuracy is greatly improved. And secondly, the robustness of the system is enhanced by random matrix transformation, so that the system can cope with a complex and changeable medical environment.
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