As the adoption rates for electronic medical records and other patient information systems grow, medical institutions are amassing ever larger collections of computerized patient data. My colleagues and I theorize that beyond the traditionally envisioned benefits of electronic records, such as error reduction and eased access to patient information, these large data repositories can be mined to extract clinically valuable information about past treatments and their associated outcomes. Moreover, if such insights can be customized to the context of a specific patient's care, this approach promises to enable a new framework for evidence-based medicine. This talk will describe the key elements of our approach toward achieving this vision and provide a glimpse of our lab's prototype evidence-based visual analytics applications.
Bio: David Gotz is a research scientist at the T.J. Watson Research Center at IBM Research. His research interests include visual analytics, intelligent user interfaces, visualization and graphics, and multimedia systems. He received his Ph.D. from UNC-Chapel Hill in 2005 where he explored scalable and adaptive streaming technologies for non-linear media. Prior to his Ph.D. studies, he received his MS in Computer Science from UNC-Chapel Hill in 2001. He graduated with highest honors from Georgia Tech in 1999 where he majored in Computer Science and received a certificate in Economics.