Machine Learning Models to Predict Risk of Maternal Morbidity and Mortality From Electronic Medical Record Data: Scoping Review
Key recommendations for future research and practice include expanding efforts to study maternal morbidity and mortality outcomes in the postpartum period, increasing transparency and reproducibility of studies through use of reporting checklists, and expanding efforts to implement ML models in clinical practice.
Authors
- Lavanya Vasudevan (ORCID: https://orcid.org/0000-0003-2900-6070)
- Alison M. Stuebe (ORCID: https://orcid.org/0000-0002-1228-4587)
- Jamie Conklin (ORCID: https://orcid.org/0000-0002-3527-9975)
- Safoora Masoumi (ORCID: https://orcid.org/0000-0002-7343-1771)
- Avelino Victor
- Lauren M. Kucirka (ORCID: https://orcid.org/0000-0001-5518-4546)
- Subha Balasubramanian (ORCID: https://orcid.org/0009-0007-8540-2701)
- David Page (ORCID: https://orcid.org/0000-0003-0576-2912)
- Metin N. Gürcan (ORCID: https://orcid.org/0000-0002-2421-8229)
- Karl Shieh
- Mao‐Ling Wei (ORCID: https://orcid.org/0000-0002-8124-6218)
- Mohammad Golam Kibria (ORCID: https://orcid.org/0000-0002-7018-1180)
Institutions
- University of North Carolina at Chapel Hill (US)
- Emory University (US)
- Duke University (US)
- Duke Institute for Health Innovation (US)
- Wake Forest University (US)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2025-08-14
- DOI
- https://doi.org/10.2196/68225
- Citations
- 7
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 17.37