A machine learning approach to investigate potential risk factors for gastroschisis in California.

Journal: Birth defects research
Published Date: Mar 1, 2019

Abstract

BACKGROUND: To generate new leads about risk factors for gastroschisis, a birth defect that has been increasing in prevalence over time, we performed an untargeted data mining statistical approach.

Authors

  • Kari A Weber
    Department of Pediatrics, Division of Neonatology, Stanford University School of Medicine, Stanford, California.
  • Wei Yang
    Key Laboratory of Structure-Based Drug Design and Discovery (Shenyang Pharmaceutical University), Ministry of Education, School of Traditional Chinese Materia Medica, Shenyang Pharmaceutical University, Wenhua Road 103, Shenyang 110016, PR China. Electronic address: 421063202@qq.com.
  • Suzan L Carmichael
    March of Dimes Prematurity Center, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA.
  • Amy M Padula
    Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Francisco, California.
  • Gary M Shaw
    March of Dimes Prematurity Center, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA.

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