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Predicting newborn health outcomes with electronic health records data

Researchers used electronic health records (EHRs) and advanced machine learning methodologies to predict neonatal outcomes from maternal conditions. The study used data from 32,354 mother-newborn dyads to train and validate the model and found it outperformed currently used EHR-based clinical risk scores.

The study identified previously unreported associations between maternal conditions, such as anemia, certain medication exposures, and social determinants of health, with neonatal outcomes. A longitudinal clinical risk calculator to predict neonatal outcomes was developed in the study , enabling individualized care and aiding in the identification of suitable patients for clinical trials. It can be accessed at

Ref: De Francesco D, Reiss JD, Roger J, et al. Data-driven longitudinal characterization of neonatal health and morbidity. Science Translational Medicine. 2023;15(683). doi:

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