Machine Learning Estimation of Gestational Age at Delivery Using Linked Mother–Infant Electronic Health Records Across Two Health Systems

Purpose This study aimed to develop and evaluate machine‐learning models for estimating gestational age at delivery using EHR data from two health systems. Methods We trained random forest, gradient boosting, and ensemble models using EHR data from mother–infant dyads at Vanderbilt University Medical Center (VUMC) and independently reimplemented and retrained the modeling pipeline at the 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 gestational‐age category at delivery. 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 showed high agreement with the reference standard on the VUMC dataset (±1 week: 85.1%, ±2 weeks: 94.3%, MAE: 4.4 days). When the same modeling pipeline was independently implemented and retrained on the UMich dataset, the ensemble models achieved higher agreement with the reference standard (±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 overall, although performance was substantially lower for preterm deliveries. Reproducibility of the modeling approach and portability of the analytic workflow were demonstrated across the two health systems. Conclusion This study presents a robust machine learning framework to estimate gestational age at delivery, whose development process was reproducible across two health systems. The framework has the potential to support large‐scale real‐world maternal and neonatal health studies requiring retrospective estimation of gestational age.