Early prediction of preeclampsia using the first trimester vaginal microbiome
William F. Kindschuh, George I. Austin, Yoli Meydan, Heekuk Park, Julia A. Urban, Erin Watters, Susan V. Pollak, George Robert Saade, Judith M. Chung, Brian M. Mercer, William A. Grobman, David M. Haas, Robert M. Silver, Myrna G. Serrano, Gregory A. Buck, , Tal Korem
This study predicts preeclampsia using first trimester vaginal microbiome data with machine learning, achieving an auROC of 0.78 in the training set and 0.80 in an independent cohort.
bioRxiv · Dec 1, 2024 · PreprintMicrobiology