Accessibility settings

This paper is in the following e-collection/theme issue:

Digital Health Reviews (1864) Clinical Informatics (1476) Obstetrics, Gynecology, and Reproductive Medicine (352) Electronic Health Records (1406) Machine Learning (2293) Pregnancy Information, Education and Lifestyle Interventions (430)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/68225, first published .
Machine Learning Models to Predict Risk of Maternal Morbidity and Mortality From Electronic Medical Record Data: Scoping Review

Machine Learning Models to Predict Risk of Maternal Morbidity and Mortality From Electronic Medical Record Data: Scoping Review

Machine Learning Models to Predict Risk of Maternal Morbidity and Mortality From Electronic Medical Record Data: Scoping Review

Authors of this article:

Lavanya Vasudevan1, 2 Author Orcid Image ;   Mohammad Golam Kibria3 Author Orcid Image ;   Lauren M Kucirka4, 5 Author Orcid Image ;   Karl Shieh4 Author Orcid Image ;   Mian Wei6 Author Orcid Image ;   Safoora Masoumi3 Author Orcid Image ;   Subha Balasubramanian7 Author Orcid Image ;   Ashley Victor3 Author Orcid Image ;   Jamie L Conklin8 Author Orcid Image ;   Metin Nafi Gurcan9 Author Orcid Image ;   Alison M Stuebe4, 10 Author Orcid Image ;   David Page6 Author Orcid Image
Article Authors Cited by (8) Tweetations Metrics

Journals

  1. Carvalho e Silva M, Figueiredo Delgado A, Carvalho e Silva F. Machine learning identification of factors associated with exclusive breastfeeding in Brazilian infants: Cross-sectional analysis of the ENANI-2019 survey. International Journal of Medical Informatics 2026;206:106126 View
  2. Noor S, Islam R, Yeasar S, Siddique S. Machine learning-based prediction of maternal continuum of care completion: Evidence from Bangladesh Demographic and Health Survey 2022. Array 2026;29:100666 View
  3. Sani J, Alhur A, Ahmed M. Machine Learning-Based Prediction of Institutional Delivery Dropout (IDD) Among Nigerian Women: An Exploratory Study Using SHAP Interpretability. Journal of Epidemiology and Global Health 2026;16(1) View
  4. Ji Q, Wang M. AI-driven high-risk pregnancy prediction: balancing early detection, anxiety, and discrimination in digital public health. Frontiers in Public Health 2026;14 View
  5. Li Q, Zeng M, Liang D, Zheng X, Zhang X, Martin-Payo R. Research on machine learning-based clinical prediction models: a bibliometric analysis. Frontiers in Oncology 2026;16 View
  6. Gedefaw A, Biwota G, Mesele A, Mengistu A, Teferi G. Mortality Prediction Among People Living With HIV on Antiretroviral Therapy in Public Health Facilities in Gondar City Administration, Northwest Ethiopia: Machine Learning–Based Study. JMIR Medical Informatics 2026;14:e78770 View

Conference Proceedings

  1. Kanuri D, Kota M, P T, Sambath N, S P S. 2025 International Conference on Sustainable Communication Networks and Application (ICSCN). Multimodel Data Integration in Maternal Health View
  2. Jeevitha V, R P. 2025 10th International Conference on Communication and Electronics Systems (ICCES). Early Prediction of Postpartum Depression Using Machine Learning on Electronic Health Records View