Intelligent severe case early warning analysis system based on multi-source clinical data fusion

Through the method of fusion of multi-source clinical data, multi-dimensional physiological feature vectors are generated, which solves the problem of excessive image smoothing in traditional respiratory intensive monitoring, and realizes accurate warnings for abnormal changes in respiratory intensive patients, improving the accuracy and sensitivity of monitoring.

CN120376141AInactive Publication Date: 2025-07-25THE FIRST PEOPLES HOSPITAL OF NANTONG
0 Cites 1 Cited by

Patent Information

Application Number
CN202510464259.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-14
Publication Date
2025-07-25
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The traditional monitoring methods for severe respiratory patients rely on Gaussian filtering at fixed windows, resulting in excessive smoothing of images and difficulty in accurately identifying abnormal changes in patients, reducing the reliability and accuracy of respiratory monitoring early warning.

Method used

Using the method of multi-source clinical data fusion, multi-dimensional physiological feature vectors are generated through the respiratory motion analysis module, the ventilator waveform analysis module and the blood oxygen signal processing module, and combined with the risk assessment module and the alarm trigger module, accurate assessment of the respiratory compensation state and hierarchical alarm are realized.

Benefits of technology

It improves the sensitivity and reliability of respiratory monitoring, can accurately warn of abnormal changes in respiratory critically ill patients, reduces the risk of misjudgment, and improves the accuracy and sensitivity of monitoring.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention discloses an intelligent critical case early warning analysis system based on multi-source clinical data fusion, belongs to the technical field of image processing, and can more accurately carry out respiration monitoring and early warning. Comprising a respiratory movement analysis module used for generating a respiratory movement amplitude change curve according to thoracic and abdominal movement video data; the breathing machine waveform analysis module is used for generating waveform characteristic data containing abnormal time slices according to the breathing machine waveform data; the blood oxygen signal processing module is used for generating a blood oxygen change trend vector according to the blood oxygen saturation degree signal data; the multi-modal fusion module is used for generating a multi-dimensional physiological feature vector under a synchronous timestamp according to the breathing movement amplitude change curve, the waveform feature data and the blood oxygen change trend vector; the risk assessment module is used for generating a breathing compensation state score according to the multi-dimensional physiological feature vector; and the alarm triggering module is used for triggering a grading alarm signal according to the deviation degree of the breathing compensation state score and the baseline range.
Need to check novelty before this filing date? Find Prior Art