AI in Hypertensive Disorders of Pregnancy: Review

ML is increasingly applied to HDP, with significant growth in diagnostic and risk prediction models. However, geographic disparities, limited phenotype representation, and models to help intervene at critical time points throughout the perinatal lifecycle remain barriers. Notably, models addressing time-to-intervene predictions and hospital readmissions are underrepresented, highlighting critical gaps in the current literature. Addressing these limitations-by developing models to help improve the timing of medical interventions, higher risk profiling, and diverse datasets-can advance ML's role in improving maternal and fetal outcomes and reducing mortality globally. Future research should focus on refining ML models to support clinicians and advance care for patients with HDP.

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Publication Details

Journal
American Journal of Hypertension
Published
2025-03-30
DOI
https://doi.org/10.1093/ajh/hpaf052
Citations
2
Primary Topic
Pregnancy and preeclampsia studies
Type
article
Field-Weighted Citation Impact
4.11
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article

AI in Hypertensive Disorders of Pregnancy: Review

Tony Wen, Hussein M. Ali, Adetola Louis‐Jacques, Michaela Rechdan et al.
2 citations
American Journal of Hypertension
Pregnancy and preeclampsia studies
4.11
article

AI in Hypertensive Disorders of Pregnancy: Review

Tony Wen, Hussein M. Ali, Adetola Louis‐Jacques, Michaela Rechdan, Elizabeth Shenkman, François Modave, Sophie Beneteau, Dominick J. Lemas, Ruben Zapata, Michele Himadi, Steven M. Smith, Tamara N. Kolli, L. Brinkley, Richard R. Reich, Tioluwani Tolani
article en
2 citations

Abstract

ML is increasingly applied to HDP, with significant growth in diagnostic and risk prediction models. However, geographic disparities, limited phenotype representation, and models to help intervene at critical time points throughout the perinatal lifecycle remain barriers. Notably, models addressing time-to-intervene predictions and hospital readmissions are underrepresented, highlighting critical gaps in the current literature. Addressing these limitations-by developing models to help improve the timing of medical interventions, higher risk profiling, and diverse datasets-can advance ML's role in improving maternal and fetal outcomes and reducing mortality globally. Future research should focus on refining ML models to support clinicians and advance care for patients with HDP.

American Journal of HypertensionVol. 38(12)
Florida Department of Health (US), University of Florida Health (US), University of Florida (US), Dr. Herbert & Nicole Wertheim Family Foundation (US), Wake Forest University (US), Florida Agricultural and Mechanical University (US), American University of the Caribbean School of Medicine (SX), Florida College (US)
Good health and well-being
Openalex Percentile: Top 8%
Pregnancy and preeclampsia studies
4.11
DOISourceOpenAlex
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