A Machine Learning-Based Triage Tool for Children With Acute Infection in a Low Resource Setting.

Journal: Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies
PMID: 31805020

Abstract

OBJECTIVES: To deploy machine learning tools (random forests) to develop a model that reliably predicts hospital mortality in children with acute infections residing in low- and middle-income countries, using age and other variables collected at hospital admission.

Authors

  • Arthur Kwizera
    Department of Anaesthesia and Critical Care, Makerere University College of Health Sciences, Kampala, Uganda.
  • Niranjan Kissoon
    BC Children's Hospital, University of British Columbia, Vancouver, BC, Canada.
  • Ndidiamaka Musa
    Seattle Children's Hospital, University of Washington, Seattle, WA.
  • Olivier Urayeneza
    Gitwe Hospital and Gitwe School of Medicine, Gitwe, Rwanda.
  • Pierre Mujyarugamba
    Gitwe Hospital and Gitwe School of Medicine, Gitwe, Rwanda.
  • Andrew J Patterson
    Department of Anesthesiology, Emory University, Atlanta, GA.
  • Lori Harmon
    Society of Critical Care Medicine on behalf of the Surviving Sepsis Campaign, Mount Prospect, IL.
  • Joseph C Farmer
    Department of Critical Care Medicine, Mayo Clinic, Phoenix, AZ.
  • Martin W Dünser
    Department of Anesthesiology and Intensive Care Medicine, Kepler University Hospital and Johannes Kepler University Linz, Linz, Austria.
  • Jens Meier
    Department of Anesthesiology and Intensive Care Medicine, Faculty of Medicine of the Kepler University Linz, Krankenhausstraße 9, 4020 Linz, Austria. Electronic address: jens.meier@gmail.com.

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