Two Medical Informatics Applications of Machine Learning
Speaker: Christian Shelton
Affiliation: University of California - Riverside
Abstract: Hospital intensive care units (ICUs) are particularly interesting medical microcosm as their data are relatively frequent and some types of outcomes are more quickly known. They provide a glimpse into the future challenges and opportunities that await as medical data collection becomes more pervasive. In this talk, I will discuss current sources of ICU data and their properties and challenges, and then describe two applications of machine learning to these data, both joint work with Children’s Hospital Los Angeles. The first application models the ICU outcomes for patients based on their signs and symptoms on arrival. The goal is to make data-informed triage decisions in mass casualty scenarios. The second application models the blood gas levels for critically ill children on mechanical ventilation. The goal is to reduce the number of invasive tests necessary.
Biography: Christian Shelton is an Associate Professor of Computer Science at the University of California at Riverside. He joined the faculty in 2003. His research interest is in statistical approaches to artificial intelligence, mainly in the areas of machine learning and dynamic processes. He has been the Managing Editor of the Journal of Machine Learning Research and on the editorial board of the Editorial Board of the Journal of Artificial Intelligence Research.
Dr. Shelton received his B.S. in Computer Science from Stanford University in 1996 and his Ph.D. from MIT in 2001. From 2001 to 2003, he was a post doctoral scholar back at Stanford. He has been a visiting researcher at Intel Research (2003-2004) and Children’s Hospital Los Angeles (2012-2013).
For more information, contact Prof. Mihaela van der Schaar (mihaela@ee.ucla.edu)
Date/Time:
Date(s) - Jan 20, 2016
11:00 am - 12:30 pm
Location:
E-IV Faraday Room #67-124
420 Westwood Plaza - 6th Flr., Los Angeles CA 90095