Machine‐learning‐based prediction of pre‐eclampsia using first‐trimester maternal characteristics and biomarkers

To evaluate the accuracy of predicting the risk of developing pre‐eclampsia (PE) according to first‐trimester maternal demographic characteristics, medical history and biomarkers using artificial intelligence and machine‐learning methods. The detection rate for preterm PE vs no PE, at a 10% FPR, was 53.3% when screening by maternal factors only. The corresponding AUC was 0.816; these increased to 75.3% and 0.909, respectively, with the addition of biomarkers into the model. Screening for PE using a non‐linear machine‐learning‐based approach does not require a population‐based normalization, and its performance is similar to logistic regression. Removing race information from the model reduces its prediction accuracy, especially for non‐white populations with maternal factors.