Learning Effective Representations with Electronic Health Records
报告题目: Learning Effective Representations
with Electronic Health Records
Data-Driven Healthcare (DDH) has aroused considerable interests from various research fields in recent years. Patient Electronic Health Records (EHR) is one of the major carriers for conducting DDH research. There are a lot of challenges on working directly with EHR, such as sparsity, high-dimensionality, and temporality. In this talk I will introduce my recent works on learning effective representations for EHR including: 1) a grouping scheme to get higher level EHR representations 2) temporal pattern extraction to explore the event temporalities of EHR. We will show various applications of those techniques including early prediction of the onset risk of chronic diseases and disease progression modeling.
Fei Wang is currently an associate professor in Department of Computer Science and Engineering, University of Connecticut. He also affiliates with University of Connecticut Health Center. Before his current position, he worked in IBM T. J. Watson Research Center for 4.5 years. His major research interest is data analytics and its applications in biomedical informatics. He regularly publishes papers on top data mining conferences like KDD, ICDM and SDM, as well as medical informatics conferences like AMIA. His papers have received nearly 2,500 citations so far. He won best research paper nomination for ICDM 2010, Marco Romani Best paper nomination in AMIA TBI 2014, and best paper finalist for SDM 2011 and 2015.