Avoidable Serum Potassium Testing in the Cardiac ICU: Development and Testing of a Machine-Learning Model.

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: 33332868

Abstract

OBJECTIVES: To create a machine-learning model identifying potentially avoidable blood draws for serum potassium among pediatric patients following cardiac surgery.

Authors

  • Bhaven B Patel
    Harvard Institute for Applied Computational Science, Harvard University, Cambridge, MA.
  • Francesca Sperotto
    Harvard Institute for Applied Computational Science, Harvard University, Cambridge, MA.
  • Mathieu Molina
    Computational Health Informatics Program, Boston Children's Hospital, Boston, MA.
  • Satoshi Kimura
    Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA.
  • Marlon I Delgado
    Department of Cardiology, Boston Children's Hospital, Boston, MA.
  • Mauricio Santillana
    Harvard School of Engineering and Applied Sciences, Cambridge, Massachusetts, United States of America; Boston Children's Hospital Informatics Program, Boston, Massachusetts, United States of America; Harvard Medical School, Boston, Massachusetts, United States of America.
  • John N Kheir
    Department of Cardiology, Boston Children's Hospital, Boston, MA.

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