Machine Learning Estimation of Gestational Age at Delivery Using Linked Mother–Infant Electronic Health Records Across Two Health Systems
Year: 2026
Type: article
Abstract: Objective: This study aimed to train and evaluate supervised machine learning algorithms using electronic health record (EHR) data to accurately estimate gestational age at delivery. Materials and Methods: We trained random forest, gradient boosting, and ensemble models on EHR data of mother-infant dyads from Vanderbilt University Medical Center(VUMC) and replicated the analyses at University of Michigan (UMich). We further analyzed EHR predictors of gestational age, assessed temporal drift in EHR data elements, and evaluated model performance stratified by delivery status. Results: The study included pregnancies corresponding to 54,344 and 34,345 mother-infant dyads at VUMC (2005-2025) and UMich (2012-2024), respectively. The gestational age predictions of the ensemble models achieved the highest agreement with the reference standard on the VUMC dataset (±1 week: 85.2%, ±2 weeks: 94.3%, MAE: 4.4 days) and demonstrated stronger generalization on the UMich dataset (±1 week: 93.1%, ±2 weeks: 97.8%, MAE: 2.8 days). Further, performance was better among pregnancies delivered in more recent years, and among full- and late-term deliveries compared with preterm deliveries. Discussion: The results indicate that supervised machine learning methods leveraging linked mother-infant EHRs can accurately estimate gestational age at delivery, while demonstrating the generalizability of the modeling approach and the portability of the analytic workflow across healthcare sites. Conclusion: This study presents a robust and generalizable machine learning framework to estimate gestational age at delivery. The framework can be reliably used to impute gestational age in large-scale, real-world clinical studies to support maternal and neonatal health research, in which accurate estimation of pregnancy onset is critical.
DOI: 10.1002/pds.70516
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,650
Funders: National Institutes of Health, National Human Genome Research Institute, NIH Office of the Director, National Institute of General Medical Sciences, National Institute of Allergy and Infectious Diseases, U.S. National Library of Medicine, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: DR. EPS: Drug Repurposing for Extended Patient Survival, An Integrative Omics Approach to Identify Biomarkers Related to Preeclampsia and Breast Cancer Risks, Genetic risk and long-term outcomes associated with Drug-induced Stevens-Johnson syndrome and toxic epidermal necrolysis in survivors, Longitudinal investigation of neuroplasticity during pregnancy using multiple imaging modalities, Leveraging artificial intelligence methods and electronic health records for pediatric pharmacovigilance, Cancer precision medicine through spatially informative single cell image and transcriptomics data analysis, Personalized cancer drug repurposing using single cell RNA-Seq data, Immune-mediated adverse drug reactions to HIV and TB treatments in South Africa: predict, prevent and improve long-term outcomes (IMARI SA study), SCAR 2025: Stronger Together, Biomedical Informatics and Data Science Training Program (BIDS-TP), NATIENS: A Phase III Randomized Double Blinded Study to Determine the Mechanisms and Optimal Management of Stevens-Johnson Syndrome and Toxic Epidermal Necrolysis
OpenAlex ID: W7162333774