AI in Hypertensive Disorders of Pregnancy: Review.

Ruben D Zapata¹, Tioluwani Tolani², Rebecca Reich³, Sophie Beneteau⁴, Hana Ali⁵, Tanmayee Kolli⁵, Michaela Rechdan⁶, Lindsey Brinkley⁶, Michele Himadi⁶, Adetola Louis-Jacques^7,8, Francois Modave⁹, Steven M Smith^6,10, Tony Wen⁷, Elizabeth Shenkman¹, Dominick J Lemas^1,7,8

¹Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.

²American University of the Caribbean School of Medicine, Cupecoy, Sint Maarten.

³Department of Food Science and Human Nutrition, College of Agricultural and Life Sciences, University of Florida, Gainesville, Florida, USA.

⁴Department of Mathematics, College of Liberal Arts and Sciences, University of Florida, Gainesville, Florida, USA.

⁵Department of Biomedical Engineering, Herbert Wertheim College of Engineering, Univerisity of Florida, Gainesville, Florida, USA.

⁶Department of Medicine, College of Medicine, University of Florida, Gainesville, Florida, USA.

⁷Department of Obstetrics/Gynaecology, University of Florida, Gainesville, Florida, USA.

⁸College of Medicine, University of Florida Center for Perinatal Outcomes Research, Gainesville, Florida, USA.

⁹Department of Implementation Science, Wake Forest University, Winston-Salem, North Carolina, USA.

¹⁰Department of Pharmaceutical Outcomes & Policy, College of Pharmacy, University of Florida, Gainesville, Florida, USA.

PubMed DOI

Abstract

BACKGROUND: Hypertensive disorders of pregnancy (HDP) are a leading cause of maternal and fetal mortality worldwide. Early detection and risk stratification are critical for timely intervention to prevent severe complications such as eclampsia, stroke, and preterm delivery. However, traditional clinical methods often lack the precision needed to identify high-risk individuals effectively. Machine…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free Account Sign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.