Title: A Systems Based Data Science Approach to Studying Acute and Critical Illness

Abstract: This talk will cover the challenges and opportunities in utilizing multi-modal data, often consisting of irregular time series, observational and experimental data, to develop predictive models of complex and dynamic clinical course among critically ill patients in the intensive care unit (ICU). The ICU is a data intensive environment, which contains a large number of devices and interfaces that continuously capture data streams. However much of this data is curated based on the clinical suspicion of the care provider, thus patients presenting with the same condition may have multiple permutations of data collection. In this talk, I highlight the challenges and opportunities in modeling complex problems related to critical illness and the approaches we utilize to better understand how data can inform clinical decision making at the bedside.

Bio: Rishi Kamaleswaran is an Associate Professor at Emory University, Department of Biomedical Informatics, with secondary appointments in Pediatrics and Emergency Medicine. He earned his Ph.D. in Computer Science from the University of Ontario Institute of Technology in Canada. He is the Director of Translational Informatics within the School of Medicine and the Georgia CTSA. He is the Co-Director of the NIH P30 Georgia Cystic Fibrosis Research and Translation Core Center where he oversees a number of clinical informatics research. He also oversees the Emory Real-Time Data Science and Decision Support (RADS2) center that advances AI/ML applications at the bedside. His research is funded by a number of federal and regional agencies including the National Institutes of Health, Veteran's Affairs, Department of Defense, Michael J. Fox Foundation, and the CF foundation.