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PregMedNet: Multifaceted maternal medication impacts on neonatal complications
Yeasul Kim1,2,3, Ivana Marić4, Chloe M Kashiwagi5,4,6
1Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford School of Medicine, Stanford, CA, USA. ykim824@stanford.edu.
Nature Communications
|July 10, 2026
Summary
This study introduces PregMedNet, a platform analyzing maternal medication safety during pregnancy. It identifies drug-related risks to newborns using nationwide data and machine learning, enhancing perinatal care.
Area of Science:
- Perinatal pharmacology
- Computational epidemiology
- Drug safety science
Background:
- Medication use during pregnancy is common, yet safety data is limited due to ethical and practical challenges in studying pregnant populations.
- Digitized healthcare data and advanced computational methods now enable large-scale retrospective drug safety evaluations.
Purpose of the Study:
- To develop and present PregMedNet, a platform for characterizing maternal medication associations with neonatal outcomes.
- To systematically analyze drug-disease pairs, medications, and neonatal outcomes using nationwide claims data.
Main Methods:
- Utilized nationwide claims data and an advanced machine learning pipeline for analysis.
- Covered over 27,000 drug-disease pairs, 1,152 medications, and 24 neonatal outcomes.
- Employed graph learning to explore potential biological mechanisms.
Main Results:
- Characterized multifaceted maternal medication associations with neonatal outcomes.
- Identified known and novel odds ratios (ORs), adjusted ORs, and drug-drug interactions.
- One identified association was corroborated by in vivo experiments, validating the platform's findings.
Conclusions:
- PregMedNet provides extensive, multifaceted drug safety information for a previously underrepresented population.
- The platform demonstrates the utility of claims data and machine learning in perinatal medication safety research.
- Findings are expected to advance maternal medication safety and improve neonatal outcomes.
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