Dr. Ivana Maric is a Senior Research Scientist at the Prematurity Research Center at Stanford University, School of Medicine. Her research focus is on applying machine learning to improving maternal and perinatal health. She is a co-recipient of the 2021 Rosenkranz Prize and the 2013 IEEE Communications Society Best Tutorial Paper Award.
Machine Learning for Early Prediction of Pregnancy-Related Outcomes
More than 800 women worldwide die from pregnancy-related causes every day. The complexity of adverse outcomes such as preeclampsia and preterm birth makes their early prediction difficult. We present a machine learning methodology that, by analyzing large omics and EHR data can identify a handful of predictive biomarkers ultimately leading to a simple diagnostic tool.
Linda Giudice, UCSF
Yoel Sadovsky, UPMC
Researchers have long been recognizing the uniqueness of women’s health and its substantial effect on clinical practice, acknowledging the increasing appreciation of the importance of multidisciplinary approaches to health and disease. In every organ system, there are diseases that are unique to women, more common in women than in men, or characterized by differences in disease course in women compared to men. This Track will focus on the following topics related to Women’s Health: