Performance of Fetal Medicine Foundation Software for Pre-Eclampsia Prediction Upon Marker Customization: Cross-Sectional Study

Journals

1. 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
2. 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
3. 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
4. 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
5. Shifman E, Pylaeva N, Gulyaev V, Kulikov A, Pylaev A, Kazinina E, Prochan E. Possibilities of Predicting the Manifestation of HELLP Syndrome. Ural Medical Journal 2024;23(3):179 View