Machine Learning Models for Predicting Neonatal Mortality: A Systematic Review.

Journal: Neonatology
PMID: 34261070

Abstract

INTRODUCTION: Approximately 7,000 newborns die every day, accounting for almost half of child deaths under 5 years of age. Deciphering which neonates are at increased risk for mortality can have an important global impact. As such, integrating high computational technology (e.g., artificial intelligence [AI]) may help identify the early and potentially modifiable predictors of neonatal mortality. Therefore, the objective of this study was to collate, critically appraise, and analyze neonatal prediction studies that included AI.

Authors

  • Cheyenne Mangold
    Department of Pediatrics, University of Texas Health San Antonio, San Antonio, Texas, USA.
  • Sarah Zoretic
    Department of Pediatrics, University of Texas Health San Antonio, San Antonio, Texas, USA, sarahzoretic@gmail.com.
  • Keerthi Thallapureddy
    Department of Pediatrics, University of Texas Health San Antonio, San Antonio, Texas, USA.
  • Axel Moreira
    Department of Pediatrics, Baylor College of Medicine, Houston, Texas, USA.
  • Kevin Chorath
    Department of Otolaryngology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Alvaro Moreira
    Department of Pediatrics, University of Texas Health San Antonio, San Antonio, Texas, USA.

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