Nima Aghaeepour is an Associate Professor at Stanford University. His laboratory develops machine learning and artificial intelligence methods to study clinical and biological modalities in translational settings. He is primarily interested in leveraging multiomics studies, wearable devices, and electronic health records to address global health challenges. His work is recognized by awards from numerous national and international organizations including the Bill and Melinda Gates Foundation, the March of Dimes Foundation, the Burroughs Wellcome Fund, the National Institute of General Medical Sciences, and the National Center for Advancing Translational Sciences.
An AI-Driven Taxonomy for Prematurity
Despite significant investments, preterm birth has remained the single largest cause of death in children under 5 years of age. I will discuss a series of studies using state-of-the-art biological profiling, electronic health records, and artificial intelligence techniques which aim to enable an integrated precision-medicine approach to predict, prevent, and manage preterm births.
Track 3, January 26
Yoel Sadovsky, UPMC
Aleksandar Rajkovic, UCSF
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: