Postoperative neonatal mortality prediction using superlearning.

Journal: The Journal of surgical research
Published Date: Jan 1, 2018

Abstract

BACKGROUND: The variable risks associated with neonatal surgery present a challenge to accurate mortality prediction. We aimed to apply superlearning, an ensemble machine learning method, to the prediction of 30-day neonatal postoperative mortality.

Authors

  • Jennifer N Cooper
    Center for Surgical Outcomes Research and Center for Innovation in Pediatric Practice, The Research Institute at Nationwide Children's Hospital, Columbus, Ohio. Electronic address: jennifer.cooper@nationwidechildrens.org.
  • Peter C Minneci
    Center for Surgical Outcomes Research and Center for Innovation in Pediatric Practice, The Research Institute at Nationwide Children's Hospital, Columbus, Ohio; Department of Surgery, Nationwide Children's Hospital, Columbus, Ohio.
  • Katherine J Deans
    Center for Surgical Outcomes Research and Center for Innovation in Pediatric Practice, The Research Institute at Nationwide Children's Hospital, Columbus, Ohio; Department of Surgery, Nationwide Children's Hospital, Columbus, Ohio.

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