Postoperative end risk early warning and related equipment based on multimodal monitoring information

Abstract

The invention relates to postoperative END risk early warning of multimodal monitoring information and related equipment. The device includes: a memory and a processor; the processor is configured to perform the following operations: acquire clinical data of the user to be tested in the current time period; input the clinical data into a deep autoencoder based on convolution theory to obtain the clinical data Corresponding characteristic data; use the statistical process control method to obtain the statistical value of the clinical data, compare the statistical value of the clinical data with the warning threshold corresponding to the current time period, and realize the postoperative END of the next time period according to the comparison result Risk Warning. This application realizes the real-time update of postoperative END early warning.

Description

technical field [0001] The present invention relates to the technical field of bioinformatics, and more specifically, relates to multi-modal monitoring information for automatic early warning of early neurological deterioration after endovascular treatment of acute ischemic stroke, electronic equipment, and a computer-readable storage medium. Background technique [0002] Although endovascular therapy has been recommended by many international organizations and international guidelines at present, the highest level of treatment is recommended. However, not all patients can benefit from it, and about 40% of patients who receive endovascular therapy still develop poor prognosis after surgery. Many patients experience unanticipated disease deterioration after surgery, which ultimately manifests as a poor prognosis. Early neurological deterioration (Early Neurological Deterioration, END) is the most common and representative adverse event after endovascular therapy. Currently,...

AI Technical Summary

Problems solved by technology

However, in the actual clinic, although these complex information, multi-mode monitoring data and other multi-form data are monitored in real time and limitedly recorded, only a small part of them is screened for clinical analysis

At the same time, these multi-dimensional and complex data often require senior doctors with years of clinical experience to partially analyze, and misjudgments often occur

In addition, the individualization trend of critically ill patients is obvious, and the evaluation criteria based on ordinary patients are sometimes not applicable. It is difficult for us to use uniform standards to judge these massive data, which further increases the difficulty of analysis.

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