Event Date: February 21, 2019 16:15
Representation and Exploration of Patient Cohorts
Abstract The availability of health-care data calls for effective analysis methods which help medical experts gain a better understanding of their data. While the focus has been largely on prediction, representation and exploration of health-care data have received little attention. In this talk, we introduce CORE, a data-driven framework for medical cohort representation and exploration. CORE builds a succinct representation of a cohort by pruning insignificant events using sequence matching. It also prioritizes patient attributes to short-list a set of interesting contrast cohorts as exploration candidates. We discuss real use cases that we developed in collaboration with Grenoble hospital and show the usability of CORE in interactions with our medical partners
Bio Behrooz Omidvar-Tehrani is a postdoctoral researcher at the University of Grenoble Alpes, France. Previously, he was a postdoctoral researcher at the Ohio State University, USA. His research is in the area of data management, focusing on interactive analysis of user data. Behrooz received his PhD in Computer Science from University of Grenoble Alpes, France. He has published in several international conferences and journals including CIKM, ICDE, VLDB, EDBT, DSAA and KAIS. Also, he has been a reviewer for several conferences and journals including Information Systems, TKDE, DAMI, CIKM, ICDE, and AAAI.
