Barbara Engelhardt

Machine Learning for Hospital Patient Care: Challenges and Opportunities

Abstract: Every aspect of caring for hospital patients has been challenged by the COVID-19 epidemic. In this talk, I review both the challenges and the opportunities that arise to use machine learning to assist with hospital systems responding to pandemics. First, I discuss modeling patient time-course and understanding the trajectory of diseases. Next, I consider the abilities of ML methods to perform patient triage using prediction and forecasting methods. Then, I describe the many ways that my group and others have developed policies for care, and how this might be done with limited patient data sets as in the case of an emerging disease. Looking ahead, I describe an application of these approaches to resource allocation to address the resource limitations and overburdened hospital systems. Importantly, throughout I discuss the role of sex, race, and age in these analyses and how these demographic traits impact ML methods for hospital patient care.

Bio

Barbara E. Engelhardt, an associate professor, joined the Princeton Computer Science Department in 2014 from Duke University, where she had been an assistant professor in Biostatistics and Bioinformatics and Statistical Sciences.  She graduated from Stanford University and received her Ph.D. from the University of California, Berkeley, advised by Professor Michael Jordan.

She did postdoctoral research at the University of Chicago, working with Professor Matthew Stephens, and three years at Duke University as an assistant professor. Interspersed among her academic experiences, she spent two years working at the Jet Propulsion Laboratory, a summer at Google Research, and a year at 23andMe, a DNA ancestry service. Professor Engelhardt received an NSF Graduate Research Fellowship, the Google Anita Borg Memorial Scholarship, and the Walter M. Fitch Prize from the Society for Molecular Biology and Evolution.

As a faculty member, she received the NIH NHGRI K99/R00 Pathway to Independence Award, a Sloan Faculty Fellowship, and an NSF CAREER Award. Professor Engelhardt’s research interests involve developing statistical models and methods for the analysis of high-dimensional biomedical data, with a goal of understanding the underlying biological mechanisms of complex phenotypes and human disease.