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

PloS One
Sam GhazalRita Noumeir

Abstract

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. 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 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 (< 84%, 85% to 91% and c92% to 100%). Due to data imbalance, four different data balancing processes were applied. Then, two machine learning models (artificial neural network and Bootstrap aggregation of complex decision trees) were trained and tested on these four different balanced datasets. The best model predicted SpO2 with area under the curves < 0.75. This single center pilot study using machine learning predicti...Continue Reading

Associated Clinical Trials

Apr 24, 2020·Philippe Jouvet

References

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May 18, 2012·Critical Care : the Official Journal of the Critical Care Forum·Philippe JouvetMarc Wysocki
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Feb 7, 2018·Pediatric Critical Care Medicine : a Journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies·David BrossierPhilippe Jouvet

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Citations

Apr 24, 2020·Current Opinion in Anaesthesiology·Matthias Görges, J Mark Ansermino
Sep 29, 2020·Journal of Intensive Care Medicine·Diep NguyenEric vanSonnenberg
Nov 26, 2020·International Journal of Environmental Research and Public Health·Alberto Comesaña-CamposJosé-Benito Bouza-Rodríguez

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