2/3/14: Christian Shelton


Machine Learning and Critical Care Pediatrics

Christian Shelton
University of California, Riverside

Electronic health records provide the opportunity for data-driven medical discovery, even with all of their current flaws.  Intensive care units are particularly interesting microcosm as their data are relatively frequent and some types of outcomes are more quickly known. In this talk, I will first outline the type of data, scientific questions, and domain challenges Children's Hospital Los Angeles and my group have been working on to improve critical care.  Then I will describe one project on estimating blood gas levels for children on mechanical ventilation. This project aims to remove invasive tests and provide faster weaning off of ventilation to decrease costs and improve health.

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 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 postdoctoral scholar back at Stanford. He has been a visiting researcher atIntel Research (2003-2004) and Children's Hospital Los Angeles (2012-2013).

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