Published on in Vol 21, No 11 (2019): November
Preprints (earlier versions) of this paper are
available at
https://preprints.jmir.org/preprint/14738, first published
.
Performance of Fetal Medicine Foundation Software for Pre-Eclampsia Prediction Upon Marker Customization: Cross-Sectional Study
Performance of Fetal Medicine Foundation Software for Pre-Eclampsia Prediction Upon Marker Customization: Cross-Sectional Study
Journals
- Marić I, Tsur A, Aghaeepour N, Montanari A, Stevenson D, Shaw G, Winn V. Early prediction of preeclampsia via machine learning. American Journal of Obstetrics & Gynecology MFM 2020;2(2):100100 View
- Papatheodorou S, Yao W, Vieira C, Li L, Wylie B, Schwartz J, Koutrakis P. Residential radon exposure and hypertensive disorders of pregnancy in Massachusetts, USA: A cohort study. Environment International 2021;146:106285 View
- Thong E, Ghelani D, Manoleehakul P, Yesmin A, Slater K, Taylor R, Collins C, Hutchesson M, Lim S, Teede H, Harrison C, Moran L, Enticott J. Optimising Cardiometabolic Risk Factors in Pregnancy: A Review of Risk Prediction Models Targeting Gestational Diabetes and Hypertensive Disorders. Journal of Cardiovascular Development and Disease 2022;9(2):55 View
- Li T, Xu M, Wang Y, Wang Y, Tang H, Duan H, Zhao G, Zheng M, Hu Y. Prediction model of preeclampsia using machine learning based methods: a population based cohort study in China. Frontiers in Endocrinology 2024;15 View