Abstract: The wide adoption of electronic health records (EHR) systems has led to the availability of large clinical datasets available for precision medicine research. EHR data, linked with bio-repository, is a valuable new source for deriving real-word, data-driven prediction models of disease risk and treatment response. Yet, they also bring analytical difficulties. Precise information on clinical outcomes is not readily available and requires labor intensive manual chart review. Synthesizing information across healthcare systems is also challenging due to heterogeneity and privacy. In this talk, I’ll discuss analytical approaches for mining EHR data with a focus on denoising, scalability and transportability . These methods will be illustrated using EHR data from multiple healthcare centers.

Bio: Dr. Tianxi Cai is the John Rock Professor of Population and Translational Data Science jointly appointed in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health (HSPH) and the Department of Biomedical Informatics (DBMI), Harvard Medical School, where she directs the Translational Data Science Center for a learning healthcare system. Her recent research has been focusing on developing interpretable and robust statistical and machine learning methods for deriving precision medicine strategies and more broadly for mining large-scale biomedical data including electronic health records data.