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Machine Learning Estimation of Gestational Age at Delivery Using Linked Mother-Infant Electronic Health Records

Cosmin A Bejan1, Xiaotong Yang2, Amelie Pham3

  • 1Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.

Medrxiv : the Preprint Server for Health Sciences
|June 5, 2026
PubMed
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Supervised machine learning models accurately estimate gestational age at delivery using electronic health records. This approach is generalizable and portable across different healthcare systems for maternal and neonatal health research.

Area of Science:

  • Biomedical Informatics
  • Machine Learning in Healthcare
  • Reproductive Health Research

Background:

  • Accurate gestational age estimation is critical for maternal and neonatal health outcomes.
  • Electronic Health Records (EHRs) contain valuable data for clinical prediction but require sophisticated analysis.
  • Existing methods for gestational age estimation may have limitations in large-scale, real-world applications.

Purpose of the Study:

  • To train and evaluate supervised machine learning algorithms for accurate gestational age estimation at delivery using EHR data.
  • To assess the generalizability and portability of machine learning models across different healthcare institutions.
  • To identify key EHR predictors of gestational age and analyze temporal data drift.

Main Methods:

Keywords:
electronic health recordsgestational age estimationgradient boostingmachine learningrandom forest

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  • Trained and evaluated random forest, gradient boosting, and ensemble models using EHR data from mother-infant dyads.
  • Utilized data from Vanderbilt University Medical Center (VUMC) for training and University of Michigan (UMich) for replication.
  • Analyzed EHR predictors, assessed temporal drift, and stratified model performance by delivery status.

    • Main Results:

    • Ensemble models achieved high agreement with reference standards (VUMC: ±1 week 85.2%, ±2 weeks 94.3%; UMich: ±1 week 93.1%, ±2 weeks 97.8%).
    • Mean Absolute Error (MAE) was 4.4 days at VUMC and 2.8 days at UMich.
    • Model performance was better for recent deliveries and full/late-term compared to preterm deliveries.

    Conclusions:

    • Supervised machine learning models effectively estimate gestational age at delivery using linked mother-infant EHR data.
    • The developed framework demonstrates generalizability and portability across healthcare sites.
    • This robust ML framework can reliably impute gestational age in large-scale studies to support maternal and neonatal health research.