Machine learning-based prediction of intraoperative hypoxemia for pediatric patients.

ABSTRACT:

Background

Reducing the duration of intraoperative hypoxemia in pediatric patients by means of rapid detection and early intervention is considered crucial by clinicians. We aimed to develop and validate a machine learning model that can predict intraoperative hypoxemia events 1 min ahead in children undergoing general anesthesia.

Methods

This retrospective study used prospectively collected intraoperative vital signs and parameters from the anesthesia ventilator machine extracted every 2 s in pediatric patients undergoing surgery under general anesthesia between January 2019 and October 2020 in a tertiary academic hospital. Intraoperative hypoxemia was defined as oxygen saturation <95% at any point during surgery. Three common machine learning techniques were employed to de

SUBMITTER: Park JB 

PROVIDER: S-EPMC9977036 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature