Published on in Vol 28 (2026)
Preprints (earlier versions) of this paper are
available at
https://preprints.jmir.org/preprint/78714, first published
.
Machine Learning Prediction Models for Preeclampsia: Systematic Review and Meta-Analysis
Machine Learning Prediction Models for Preeclampsia: Systematic Review and Meta-Analysis
Journals
- Ismail M. The economic imperative of artificial intelligence in maternal and neonatal health: a review of evaluation benefits, frameworks, challenges, future perspectives, and limitations. Cost Effectiveness and Resource Allocation 2026;24(1) View
- Sivasankari K, Mohan S. Artificial Intelligence in Obstetrics and Gynecology Nursing: Clinical, Educational, and Ethical Perspectives. Cureus 2026 View
- Andonotopo W, Pramono M, Dewantiningrum J, Bachnas M, Prabowo W, Sanjaya I, Wiradnyana A, Kusuma A, Gumilar K, Akbar M, Darmawan E, Suryawan A, Putra R, Rahardjo T, Anwar A, Aldiansyah D, Nugraha L, Andanaputra W, Dharma W. Bioinformatics for Multimodal Precision Pregnancy: A Systematic Review of Causal, Foundation, and Privacy-Preserving Models for Predicting Pregnancy Complications and Fetal Health Outcomes. Telangana Journal of IMA 2026;6(1):24 View
- Perez M, Falconi L, Intriago-Pazmiño M, Bastidas-Fuertes A, Benavides J, Fuertes-Arévalo R. A Machine Learning Framework for Preeclampsia Prediction at Isidro Ayora Hospital, Ecuador. Diagnostics 2026;16(14):2147 View
- Alruwaili M, Paul-Chima U. Equity-centred, nurse-led implementation of artificial intelligence in community maternal and child health nursing: a conceptual framework for low-resource settings. Frontiers in Public Health 2026;14 View