Computational phenotyping is an emerging topic in health informatics. An important catalyst of this emergence is the increasing volume of clinical data available for analysis. However, clinical data typically consists of many disparate elements and has strong temporal dependencies, both of which require designing new machine learning algorithms that can extract useful knowledge from such data.
In this talk, Dr. Chandola will talk about some of our recent work in developing techniques to extract longitudinal phenotypes from clinical data. In particular, he will discuss three different approaches to obtain longitudinal disease subtypes for Chronic Kidney Disease (CKD) - a rising health problem in both US and worldwide.
Tuesday, April 23 at 2:00pm to 3:15pm
CSM Building, Davis Auditorium 1470 Madison Avenue