Advances in medical technology have greatly increased the amount of available information (extensive electronic health records recording patient conditions, diagnostic tests, labs, imaging exams, genomics, proteomics, treatments, outcomes etc.) that is often relevant for clinical decision support. These advances have an enormous potential of creating predictive models that are geared towards improving diagnostic/prognostic accuracy as well as therapy selection capabilities. However, a larger amount of available information comes with a larger risk of data overload and suboptimal utilization of the information. Specifically, the increase in data does not always translate into improved diagnosis/treatment selection. Hence, there is a need for clinically motivated predictive models and data mining algorithms that extract the key, actionable information from the large amount of data in order to ensure improved patient outcomes.
Date: December 11, 2010
Venue: Hilton Sutcliffe B (Located on the Lower Level)