Leveraging automated approaches to categorize birth defects from abstracted birth hospitalization data.

Journal: Birth defects research
PMID: 37932954

Abstract

BACKGROUND: The Surveillance for Emerging Threats to Pregnant People and Infants Network (SET-NET) collects data abstracted from medical records and birth defects registries on pregnant people and their infants to understand outcomes associated with prenatal exposures. We developed an automated process to categorize possible birth defects for prenatal COVID-19, hepatitis C, and syphilis surveillance. By employing keyword searches, fuzzy matching, natural language processing (NLP), and machine learning (ML), we aimed to decrease the number of cases needing manual clinician review.

Authors

  • Suzanne M Newton
    Division of Birth Defects and Infant Disorders, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
  • Samantha Distler
    Division of Birth Defects and Infant Disorders, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
  • Kate R Woodworth
    Division of Birth Defects and Infant Disorders, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
  • Daniel Chang
    Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
  • Nicole M Roth
    Division of Birth Defects and Infant Disorders, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
  • Amy Board
    Division of Birth Defects and Infant Disorders, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
  • Hailee Hutcherson
    G2S Corporation, San Antonio, Texas, USA.
  • Janet D Cragan
    National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, 4770 Buford Highway NE, Atlanta, GA, 30341-3717, USA.
  • Suzanne M Gilboa
    National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, 4770 Buford Highway NE, Atlanta, GA, 30341-3717, USA.
  • Van T Tong
    Division of Birth Defects and Infant Disorders, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.

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