An intelligent prediction and early warning system for infusion and separation of infusion

By integrating multi-sensor signals and performing intelligent analysis, the system enables early prediction and root cause tracing of infusion interruption risks, solving the problems of delayed alarms and false alarms in existing infusion monitoring systems, and improving infusion safety and the work efficiency of medical staff.

CN122163939APending Publication Date: 2026-06-09SUZHOU JINSENHE TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SUZHOU JINSENHE TECH CO LTD
Filing Date
2026-02-03
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing infusion monitoring systems cannot predict the risk of infusion interruption in an early and accurate manner, and lack in-depth fusion and deep feature mining of information from multiple sensors, resulting in frequent false alarms and invalid alarms. They cannot distinguish between different fault modes, have poor adaptability, and cannot intelligently diagnose the root cause.

Method used

The system employs simultaneous acquisition and fusion processing of signals from multiple sensors, including weight, fluid pressure, and optical sensor signals. Through intelligent analysis modules, feature vectors are generated and causal analysis is performed to construct a high-dimensional infusion status model, enabling early prediction and root cause tracing of infusion interruption risks.

Benefits of technology

It significantly improves infusion safety, reduces false alarms, provides early warning time, offers interpretable decision support, and enhances the response efficiency of medical staff and the safety of infusion therapy.

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Abstract

This invention discloses an intelligent prediction and early warning system for intravenous infusion interruption. The system synchronously acquires weight, pressure, and optical multi-source sensor signals through a data acquisition module. A feature processing module performs joint time-frequency domain analysis to construct a feature vector representing the infusion status. An intelligent analysis module matches the feature vector with an abnormal pattern library to predict the abnormality type and remaining time. A causal analysis unit, combined with a directed graph model, performs root cause tracing and verification. An early warning execution module generates tiered early warning information based on the prediction results and root causes and intelligently pushes it to the corresponding terminals. The system also has the ability to dynamically optimize the abnormal pattern library based on actual treatment feedback. This invention achieves early and accurate prediction and intelligent root cause diagnosis of intravenous infusion interruption risks, significantly reducing false alarm and false negative rates, and improving the initiative and efficiency of clinical intravenous infusion safety management.
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