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Using machine learning models to predict oxygen saturation following ventilator support adjustment in critically ill children: A single center pilot study

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  • Sam Ghazal
  • Michael Sauthier
  • David Brossier
  • Wassim Bouachir
  • Philippe A Jouvet
  • Rita Noumeir

Abstract

Background: In an intensive care units, experts in mechanical ventilation are not continuously at patient’s bedside to adjust ventilation settings and to analyze the impact of these adjustments on gas exchange. The development of clinical decision support systems analyzing patients’ data in real time offers an opportunity to fill this gap. Objective: The objective of this study was to determine whether a machine learning predictive model could be trained on a set of clinical data and used to predict transcutaneous hemoglobin oxygen saturation 5 min (5min SpO2) after a ventilator setting change. Data sources: Data of mechanically ventilated children admitted between May 2015 and April 2017 were included and extracted from a high-resolution research database. More than 776,727 data rows were obtained from 610 patients, discretized into 3 class labels (

Suggested Citation

  • Sam Ghazal & Michael Sauthier & David Brossier & Wassim Bouachir & Philippe A Jouvet & Rita Noumeir, 2019. "Using machine learning models to predict oxygen saturation following ventilator support adjustment in critically ill children: A single center pilot study," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-12, February.
    • Handle: RePEc:plo:pone00:0198921
    DOI: 10.1371/journal.pone.0198921
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    Cited by:

    1. Alberto Comesaña-Campos & Manuel Casal-Guisande & Jorge Cerqueiro-Pequeño & José-Benito Bouza-Rodríguez, 2020. "A Methodology Based on Expert Systems for the Early Detection and Prevention of Hypoxemic Clinical Cases," IJERPH, MDPI, vol. 17(22), pages 1-31, November.

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