Machine Learning for Dynamic and Short-Term Prediction of Preeclampsia Using Routine Clinical Data

Can dynamic, short-term prediction of preeclampsia in late gestation be achieved using routine data from electronic health records?In this cohort study of 58 839 pregnancies delivered at 3 NewYork-Presbyterian hospitals, prediction performance peaked at 34 weeks, demonstrating that preeclampsia in late gestation can be dynamically predicted with routinely available features.This study’s results suggest that dynamic short-term prediction of preeclampsia using routine clinical data is feasible and provides actionable lead time for timely intervention in diverse health care settings.

Corresponding Authors: Fei Wang, PhD, Department of Population Health Sciences, Weill Cornell Medicine, 425 E 61st St, New York, NY 10065 (few2001@med.cornell.edu); Zhen Zhao, PhD, Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, 525 E 68th St, Room F-701, New York, NY 10065 (zhz9010@med.cornell.edu).

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