Machine learning-based prediction of intraoperative hypoxemia for pediatric patients

Jung-Bin Park, Ho-Jong Lee, Hyun-Lim Yang, Eun-Hee Kim, Hyung-Chul Lee, Chul-Woo Jung and Hee-Soo Kim

PLOS ONE, 2023, vol. 18, issue 3, 1-14

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

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0282303

DOI: 10.1371/journal.pone.0282303

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