Published on in Vol 10, No 6 (2022): June
Preprints (earlier versions) of this paper are
available at
https://preprints.jmir.org/preprint/33835, first published
.
Journals
- Mennickent D, Rodríguez A, Opazo M, Riedel C, Castro E, Eriz-Salinas A, Appel-Rubio J, Aguayo C, Damiano A, Guzmán-Gutiérrez E, Araya J. Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications. Frontiers in Endocrinology 2023;14 View
- Medjedovic E, Stanojevic M, Jonuzovic-Prosic S, Ribic E, Begic Z, Cerovac A, Badnjevic A. Artificial intelligence as a new answer to old challenges in maternal-fetal medicine and obstetrics. Technology and Health Care 2024;32(3):1273 View
- Yu Q, Lin Y, Zhou Y, Yang X, Hemelaar J. Predicting risk of preterm birth in singleton pregnancies using machine learning algorithms. Frontiers in Big Data 2024;7 View
- Feli M, Azimi I, Sarhaddi F, Sharifi-Heris Z, Niela-Vilen H, Liljeberg P, Axelin A, Rahmani A. Preterm birth risk stratification through longitudinal heart rate and HRV monitoring in daily life. Scientific Reports 2024;14(1) View
- Abdurrahman E, Siregar K, Rikawarastuti , Sutedja I, Nasir N. Machine Learning-based Prediction Model for Adverse Pregnancy Outcomes: A Systematic Literature Review. JURNAL INFO KESEHATAN 2024;22(3):532 View
Books/Policy Documents
- Sharma P, Gupta T, Kulshrestha S, Gupta P, Choudhury A, Modi D, Sengupta A. Data-Driven Reproductive Health. View