Machine learning to predict poor school performance in paediatric survivors of intensive care: a population-based cohort study.

Journal: Intensive care medicine
Published Date: Jun 24, 2023

Abstract

PURPOSE: Whilst survival in paediatric critical care has improved, clinicians lack tools capable of predicting long-term outcomes. We developed a machine learning model to predict poor school outcomes in children surviving intensive care unit (ICU).

Authors

  • Patricia Gilholm
    Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia.
  • Kristen Gibbons
    Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia.
  • Sarah Brüningk
    Department of Biosystems Science and Engineering, ETH Zurich, 4058, Basel, Switzerland.
  • Juliane Klatt
    Department of Biosystems Science and Engineering, ETH Zurich, 4058, Basel, Switzerland.
  • Rhema Vaithianathan
    Institute for Social Science Research, The University of Queensland, Brisbane, QLD, Australia.
  • Debbie Long
    Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia.
  • Johnny Millar
    Paediatric Intensive Care Unit, The Royal Children's Hospital, Melbourne, VIC, Australia.
  • Wojtek Tomaszewski
    Institute for Social Science Research, The University of Queensland, Brisbane, QLD, Australia.
  • Luregn J Schlapbach
    Neonatal and Pediatric Intensive Care Unit, Children`s Research Center, University Children's Hospital Zurich, Zurich, Switzerland.

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