Development and External Validation of a Machine Learning Model for Prediction of Potential Transfer to the PICU.

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: 35446816

Abstract

OBJECTIVES: Unrecognized clinical deterioration during illness requiring hospitalization is associated with high risk of mortality and long-term morbidity among children. Our objective was to develop and externally validate machine learning algorithms using electronic health records for identifying ICU transfer within 12 hours indicative of a child's condition.

Authors

  • Anoop Mayampurath
    Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States.
  • L Nelson Sanchez-Pinto
    Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
  • Emma Hegermiller
    Department of Pediatrics, University of Chicago, Chicago, IL.
  • Amarachi Erondu
    Department of Pediatrics, University of Chicago, Chicago, IL.
  • Kyle Carey
    Department of Medicine, University of Chicago, Chicago, IL.
  • Priti Jani
    Department of Pediatrics, University of Chicago, Chicago, IL.
  • Robert Gibbons
    Department of Pediatrics, University of Chicago, Chicago, IL.
  • Dana Edelson
    Department of Medicine, University of Chicago, Chicago IL, United States.
  • Matthew M Churpek
    Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States.

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