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As the adoption of Electronic Health Records (EHRs) increases in the USA, the complexity of EHR data is growing dramatically. EHR data now covers diverse information about patients, including diagnosis, medication, lab results, genomic information and clinical notes. However, such large volumes of information do not readily provide accurate and succinct patient representations for effective and customized healthcare.

Prof. Joydeep Ghosh of UT ECE was the keynote speaker at the inaugural Workshop on Divergences and Divergence Learning (WDDl), held in Atlanta, June 2013. In his talk, entitled "Learning Bregman Divergences for Prediction with Generalized Linear Models," which reflects joint work with ECE and WNCG student Sreangsu Acharrya,  an efficient approach to learning a broad class of predictive models was introduced. What is most remarkable about this approach is that model parameters can be estimated even when the loss function is unknown.