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Machine learning-based prediction of intraoperative hypoxemia for pediatric patients

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

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

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  • Jung-Bin Park & Ho-Jong Lee & Hyun-Lim Yang & Eun-Hee Kim & Hyung-Chul Lee & Chul-Woo Jung & Hee-Soo Kim, 2023. "Machine learning-based prediction of intraoperative hypoxemia for pediatric patients," PLOS ONE, Public Library of Science, vol. 18(3), pages 1-14, March.
    • Handle: RePEc:plo:pone00:0282303
    DOI: 10.1371/journal.pone.0282303
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