Using machine learning to predict the risk of developing hypertensive disorders of pregnancy using a contemporary nulliparous cohort
In cohort of low-risk nulliparous pregnant individuals, a prediction model may accurately predict HDP diagnosis at the time of initiating prenatal care and aid employment of close interval monitoring and prophylactic measures earlier in pregnancy.
Authors
- Adesh Kadambi (ORCID: https://orcid.org/0000-0001-5129-1985)
- Kartik K. Venkatesh (ORCID: https://orcid.org/0000-0002-8043-556X)
- S Ebrahim (ORCID: https://orcid.org/0000-0003-3901-7160)
- Isabel Fulcher (ORCID: https://orcid.org/0000-0002-1209-824X)
- Timothy Wen (ORCID: https://orcid.org/0000-0002-1749-3142)
- Jonathan S. Schor (ORCID: https://orcid.org/0000-0002-2806-8782)
- Mark A. Clapp (ORCID: https://orcid.org/0000-0001-5568-4444)
- Ali Ebrahim (ORCID: https://orcid.org/0000-0002-4009-2128)
Institutions
- Harvard University (US)
- University of California, San Francisco (US)
- University of Toronto (CA)
- The Ohio State University Wexner Medical Center (US)
- Harvard Global Health Institute (US)
Publication Details
- Journal
- AJOG Global Reports
- Published
- 2024-08-23
- DOI
- https://doi.org/10.1016/j.xagr.2024.100386
- Citations
- 3
- Primary Topic
- Pregnancy and preeclampsia studies
- Type
- article
- Field-Weighted Citation Impact
- 1.75
Funders
- National Institute of Child Health and Human Development
- Eunice Kennedy Shriver National Institute of Child Health and Human Development