Leveraging automated approaches to categorize birth defects from abstracted birth hospitalization data
Suzanne M. Newton,
Samantha Distler,
Kate R. Woodworth
et al.
Abstract:BackgroundThe 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 a… Show more
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