Machine Learning for Prediction of Maternal Hemorrhage and Transfusion (Preprint)

Abstract

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