Decision-making tasks in healthcare settings use methods that make a number of assumptions that we know are violated in clinical data. For example, clinicians do not always act optimally; clinicians are more or less aggressive in treating patients, and patients have (often unobserved) conditions that lead to differential response to interventions. In this talk, I will walk through a handful of these violated assumptions and discuss reinforcement learning methods we have created to address these violated assumptions. I will show on a number of scenarios, including sepsis treatment and electrolyte repletion, that these methods that have more flexible assumptions than existing methods lead to substantial improvements in decision-making tasks in clinical settings.
Barbara E Engelhardt is a Senior Investigator at Gladstone Institutes and Professor at Stanford University in the Department of Biomedical Data Science. She received her B.S. (Symbolic Systems) and M.S. (Computer Science) from Stanford University and her PhD from UC Berkeley (EECS) advised my Prof. Michael I Jordan. She was a postdoctoral fellow with Prof. Matthew Stephens at the University of Chicago. She was an Assistant Professor at Duke University from 2011-2014, and an Assistant, Associate, and then Full Professor at Princeton University in Computer Science from 2014-2022. She has worked at Jet Propulsion Labs, Google Research, 23andMe, and Genomics plc. In her career, she received an NSF GRFP, the Google Anita Borg Scholarship, the SMBE Walter M. Fitch Prize (2004), a Sloan Faculty Fellowship, an NSF CAREER, and the ISCB Overton Prize (2021). Her research is focused on developing and applying models for structured biomedical data that capture patterns in the data, predict results of interventions to the system, assist with decision-making support, and prioritize experiments for design and engineering of biological systems.