Improving preeclampsia risk prediction by modeling pregnancy trajectories from routinely collected electronic medical record data.

ABSTRACT: Preeclampsia is a heterogeneous and complex disease associated with rising morbidity and mortality in pregnant women and newborns in the US. Early recognition of patients at risk is a pressing clinical need to reduce the risk of adverse outcomes. We assessed whether information routinely collected in electronic medical records (EMR) could enhance the prediction of preeclampsia risk beyond what is achieved in standard of care assessments. We developed a digital phenotyping algorithm to curate 108,557 pregnancies from EMRs across the Mount Sinai Health System, accurately reconstructing pregnancy journeys and normalizing these journeys across different hospital EMR systems. We then applied machine learning approaches to a training dataset (N = 60,879) to construct predictive models of preecla

SUBMITTER: Li S 

PROVIDER: S-EPMC9170686 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature