Machine Learning To Predict Serious Bacterial Infections in Young Febrile Infants.

Journal: Pediatrics
Published Date: Sep 1, 2020

Abstract

BACKGROUND: Recent decision rules for the management of febrile infants support the identification of infants at higher risk of serious bacterial infections (SBIs) without the performance of routine lumbar puncture. We derive and validate a model to identify febrile infants ≤60 days of age at low risk for SBIs using supervised machine learning approaches.

Authors

  • Sriram Ramgopal
    Division of Emergency Medicine, Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago and Feinberg School of Medicine, Northwestern University, Chicago, Illinois; sramgopal@luriechildrens.org.
  • Christopher M Horvat
    Division of Pediatric Critical Care Medicine, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
  • Naveena Yanamala
    1 Exposure Assessment Branch, Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, West Virginia, USA.
  • Elizabeth R Alpern

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