Eclampsia risk prediction across diverse U.S. populations using CDC data: machine learning versus ACOG checklists

Background: Various tools are employed in clinical practice to predict (pre)eclampsia. Artificial intelligence offers a promising approach for identifying people at risk. More data is needed on models trained for diverse populations. Objective: This study aimed to develop machine learning prediction...

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Главные авторы:	Anne F. Minsart, MD, PhD, MPH, Marwa Alhaj Ahmad, MSc, Lisbeth Waagstein, MSc, MPH, Rivalin Aho Glele, MSc, MPH, Nadirah Ghenimi, MD, PhD, MPH
Формат:	Artigo
Язык:	Inglês
Опубликовано:	Elsevier 2026-05-01
Серии:	AJOG Global Reports
Предметы:	artificial intelligence eclampsia machine learning maternal mortality predictive value of tests preeclampsia
Online-ссылка:	http://www.sciencedirect.com/science/article/pii/S2666577826000444
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Eclampsia risk prediction across diverse U.S. populations using CDC data: machine learning versus ACOG checklists

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