Predicting COVID-19 Outcomes from Patient Data
When
I will present our ongoing work to develop prognostic models that for each COVID-19 patient predict hospitalization, critical care needs, including ICU care and the need for mechanical ventilation, and mortality. In addition to predictions, we identify key factors (demographics, comorbidities, symptoms, and clinical diagnostic results) that are most predictive of the outcome. Our work has leveraged three sets of data: (i) clinical data from a hospital in Wuhan, China, (ii) symptoms and comorbidities from all residents of Mexico who have been tested for the SARS-CoV-2 virus, and (iii) clinical data from a coalition of hospitals in New England. For clinical data, we leveraged natural language processing methods to generate patient profiles from the predominantly text-based records. For all datasets, a variety of nonlinear and linear classification methods were applied. Linear methods, which yield the most interpretable results, include distributionally robust versions of well-known methods. I will briefly outline how these are instances of a general distributionally robust learning framework we have recently developed. Beyond predictions, I will discuss methods that can leverage the robust predictive models to make decisions and offer specific personalized prescriptions and recommendations to improve future outcomes. Zoom: https://arizona.zoom.us/j/183045568 (begins at 12:30pm AZ Time - please do not log on before that time).
About the speaker: Yannis Paschalidis is a Professor and Data Science Fellow in Electrical and Computer Engineering, Systems Engineering, Biomedical Engineering, and Computing & Data Sciences at Boston University. He is the Director of the Center for Information and Systems Engineering (CISE). He obtained a Diploma (1991) from the National Technical University of Athens, Greece, and an M.S. (1993) and a Ph.D. (1996) from the Massachusetts Institute of Technology (MIT), all in Electrical Engineering and Computer Science. He has been at Boston University since 1996. His current research interests lie in the fields of systems and control, networks, optimization, operations research, computational biology, and medical informatics.
Prof. Paschalidis' work has been recognized with a CAREER award (2000) from the U.S. National Science Foundation, the second prize in the 1997 George E. Nicholson paper competition by INFORMS, the best student paper award at the 9th Intl. Symposium of Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt 2011) won by one of his Ph.D. students for a joint paper, an IBM/IEEE Smarter Planet Challenge Award, and a finalist best paper award at the IEEE International Conference on Robotics and Automation (ICRA). His work on protein docking (with his collaborators) has been recognized for best performance in modeling selected protein-protein complexes against 64 other predictor groups (2009 Protein Interaction Evaluation Meeting). His recent work on health informatics won an IEEE Computer Society Crowd Sourcing Prize and a best paper award by the International Medical Informatics Associations (IMIA). He was an invited participant at the 2002 Frontiers of Engineering Symposium organized by the National Academy of Engineering, and at the 2014 National Academies Keck Futures Initiative (NAFKI) Conference. Prof. Paschalidis is a Fellow of the IEEE and was the founding Editor-in-Chief of the IEEE Transactions on Control of Network Systems from 2013 until 2019.
Zoom: https://arizona.zoom.us/j/183045568 (begins at 12:30pm AZ Time - please do not log on before that time).