Predictive Analytics in Healthcare: From Anomaly Detection to Development of Clinical Decision Systems

Abstract: A central question in the era of “big data” is what to do with the enormous amount of information. Predictive Analytics answer this question by providing a platform in which historical data is being learned for the best decision to be made about future. In healthcare, the ultimate goal of this process is to detect an anomaly and to develop diagnostic tools. In this talk, examples of Predictive Analytics using information-theoretic and machine learning approaches with applications in healthcare are provided. Bio: Elyas Sabeti is a Research Fellow at Michigan Institute for Data Science (MIDAS), University of Michigan (UM), Ann Arbor. Prior to joining MIDAS, Elyas was a Postdoctoral Research Fellow at Department of Computational Medicine and Bioinformatics, UM. He obtained his Ph.D. of Electrical Engineering in December 2017.