Machine Learning and Statistical Models to Predict Postpartum Hemorrhage.

Objective: To predict a woman's risk of postpartum hemorrhage at labor admission using machine learning and statistical models.

Methods: Predictive models were constructed and compared using data from 10 of 12 sites in the U.S.

Ethiopian and American Collaboration: Process, Accomplishments, and Lessons Learned.

From 2015 to 2018, the Ethiopian Society of Obstetricians & Gynecologists (ESOG), the American College of Obstetricians and Gynecologists, the Ethiopian Federal Ministry of Health, and the Center for International Reproductive Health Training at the University of Michigan collaborated to define and execute the goals of ESOG to enhance education, research, publishing, and clinical care in Ethiopia. We outline the processes used to define and execute these goals, accomplishments toward achieving them, and key lessons learned.

Gottesfeld-Hohler Memorial Foundation Risk Assessment for Early-Onset Preeclampsia in the United States: Think Tank Summary.

Preeclampsia is responsible for significant maternal and neonatal morbidity and is associated with a substantial economic burden. Aspirin has been shown to be effective in decreasing the risk of preterm preeclampsia; however, there is no consensus on the target population for aspirin prophylaxis. In May 2018, the Gottesfeld-Hohler Memorial Foundation organized a working group meeting with the goal of identifying the optimal preeclampsia risk-assessment strategy and consequent intervention in the United States.