Published on in Vol 10, No 2 (2022): February
Preprints (earlier versions) of this paper are
available at
https://preprints.jmir.org/preprint/34932, first published
.

Journals
- Shara N, Mirabal-Beltran R, Talmadge B, Falah N, Ahmad M, Dempers R, Crovatt S, Eisenberg S, Anderson K. Use of Machine Learning for Early Detection of Maternal Cardiovascular Conditions: Retrospective Study Using Electronic Health Record Data. JMIR Cardio 2024;8:e53091 View
- Vasudevan L, Kibria M, Kucirka L, Shieh K, Wei M, Masoumi S, Balasubramanian S, Victor A, Conklin J, Gurcan M, Stuebe A, Page D. Machine Learning Models to Predict Risk of Maternal Morbidity and Mortality From Electronic Medical Record Data: Scoping Review. Journal of Medical Internet Research 2025;27:e68225 View
- Awoyemi T, Tolu‐Akinnawo O, Mahtani A, Padda I, Ogunniyi K, Bolakale‐Rufai I, Olusanya A, Dorcas A, Amarachi C, Akinmoju O, Uche‐Orji C, Fattah M. Advancing cardio‐obstetric care through digital health technologies: A narrative review. ESC Heart Failure 2025 View
- Declerck J, Kılıç Ö, Emir Erol E, Mehryar S, Kalra D, de Zegher I, Celebi R. Assessing Data Quality in Heterogeneous Health Care Integration: Simulation Study of the AIDAVA Framework. JMIR Medical Informatics 2025;13:e75275 View
