Models Based On Longitudinal Healthcare Event Data
In this presentation, we will discuss a framework for analyzing data concerning healthcare events at the individual level. These events can be of various types – outpatient, emergency room, inpatient, lab, pharmaceutical etc., each corresponding to one or more diagnoses. Each event happens on a certain day (or a certain hour) and when such data is collected over a period of time, it creates an evolving point process unique to each patient. Such a point process provides information about the intensity and diversity of encounters – how frequent and how fragmented care is across multiple settings, an issue of particular concern for patients with multiple chronic conditions. In this presentation, we provide concrete examples of such datasets and the operational implications for clinicians. We will also try to seek the audience’s feedback on what machine learning techniques might be best suited to recognizing patterns in high-dimensional event sequences.
Dr. Hari Balasubramanian is Associate Professor of Industrial Engineering at the University of Massachusetts, Amherst. He received his doctoral degree at the Arizona State University in 2006. Dr. Balasubramanian spent two years as a Research Associate at Mayo Clinic in Rochester, Minnesota before joining the University of Massachusetts in 2008. His research interests are in operations research applied to healthcare. In 2013, Dr. Balasubramanian received a National Science Foundation CAREER award focused on healthcare delivery.