Data-driven longitudinal characterization of neonatal health and morbidity
June 11th, 2024
This talk will be a discussion on how we can better understand some of the rich data sources available at Stanford to improve maternal-child health using state-of-the-art machine learning strategies and techniques
Jonathan Reiss
Jon Reiss, MD is an early career investigator and Stanford neonatologist interested in the intersection of omics, machine learning and precision medicine as applied to premature neonates. He works closely with a highly interdisciplinary team including Gary Shaw, David K. Stevenson Nima Aghaeepour, Lance Prince, Karl Sylvester and Michael Snyder who each have discrete expertise in these areas. Dr.Reiss’s ultimate goal is to develop novel screening, diagnostic and therapeutic measures for critically ill, premature newborns.
Alan Chang
Alan Chang, PhD is a staff research scientist in the laboratory of Dr. Nima Aghaeepour who is interested in applying deep learning to high-leverage areas in human health with a focus on newborn health. Dr. Chang studies the intersection of deep learning, multi-omics, and electronic health record data to improve risk stratification and treatment planning for the most vulnerable newborn patients. His long-term goals are to pilot the responsible deployment of artificial intelligence-based assistive tools that interface with physicians, care team members, and patients in various hospital care settings.
