Boosting framework via clinical monitoring data to predict the depth of anesthesia.

ABSTRACT:

Background

Prediction of the depth of anesthesia is a difficult job in the biomedical field.

Objective

This study aimed to build a boosting-based prediction model to predict the depth of anesthesia based on four clinical monitoring data.

Methods

Boosting is a framework algorithm that is used to train a series of weak learners into strong learners by assigning different weights according to their classification accuracy. The input of the boosting-based prediction model included four types of clinical monitoring data: electromyography, end-tidal carbon dioxide partial pressure, remifentanil dosage, and flow rate. The output was the depth of anesthesia.

Results

The boosting framework model built in this study achieved higher prediction accuracy and a lower discret

SUBMITTER: Liu Y 

PROVIDER: S-EPMC9028611 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature