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

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 co...

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Bibliographic Details
Main Authors: Shilong Li, Zichen Wang, Luciana A. Vieira, Amanda B. Zheutlin, Boshu Ru, Emilio Schadt, Pei Wang, Alan B. Copperman, Joanne L. Stone, Susan J. Gross, Yu-Han Kao, Yan Kwan Lau, Siobhan M. Dolan, Eric E. Schadt, Li Li
Format: Article
Language:English
Published: Nature Portfolio 2022-06-01
Series:npj Digital Medicine
Online Access:https://doi.org/10.1038/s41746-022-00612-x

Internet

https://doi.org/10.1038/s41746-022-00612-x

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