Application of machine-learning to predict early spontaneous preterm birth among nulliparous non-Hispanic black and white women.

Journal: Annals of epidemiology
Published Date: Nov 1, 2018

Abstract

PURPOSE: Spontaneous preterm birth is a leading cause of perinatal mortality in the United States, occurring disproportionately among non-Hispanic black women compared to other race-ethnicities. Clinicians lack tools to identify first-time mothers at risk for spontaneous preterm birth. This study assessed prediction of early (<32 weeks) spontaneous preterm birth among non-Hispanic black and white women by applying state-of-the-art machine-learning to multilevel data from a large birth cohort.

Authors

  • Ann Weber
    March of Dimes Prematurity Center, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA. Electronic address: annweber@stanford.edu.
  • Gary L Darmstadt
    March of Dimes Prematurity Center, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA.
  • Susan Gruber
    Innovation in Medical Evidence Development and Surveillance (IMEDS), Reagan-Udall Foundation for the FDA, Washington, District of Columbia.
  • Megan E Foeller
    Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA.
  • Suzan L Carmichael
    March of Dimes Prematurity Center, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA.
  • David K Stevenson
    March of Dimes Prematurity Center, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA.
  • Gary M Shaw
    March of Dimes Prematurity Center, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA.

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