Postoperative END risk early warning and related equipment for multi-modal monitoring information

Abstract

The invention relates to postoperative END risk early warning and related equipment for multi-modal monitoring information. The equipment comprises a memory and a processor. The processor is used for executing the following operations: acquiring clinical data of a to-be-tested user in a current time period; inputting the clinical data into a depth auto-encoder based on a convolution theory to obtain feature data corresponding to the clinical data; and acquiring a statistical value of the clinical data by using a statistical process control method, comparing the statistical value of the clinical data with an early warning threshold value corresponding to the current time period, and determining postoperative END risk early warning of the next time period according to a comparison result. According to the method and the device, the postoperative END early warning is updated in real time.

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,...

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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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