H. Ahmadzia, Alexa C Dzienny, Mike Bopf, Jaclyn M Phillips, Jerome Jeffrey Federspiel, Richard Amdur, Madeline Murguia Rice, Laritza Rodriguez
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Machine Learning for Prediction of Maternal Hemorrhage and Transfusion (Preprint)
Objectives: To improve PPH prediction and to compare machine learning and traditional statistical methods. Design: Cross-sectional Setting: Deliveries across US hospitals Population: Deliveries across 12 US hospitals from the 2002-2008 Consortium for Safe Labor dataset Method: We developed models using the Consortium for Safe Labor dataset. Fifty antepartum and intrapartum characteristics and hospital characteristics were included. Logistic regression, support vector machines, multi-layer perceptron, random forest
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