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There has been an increasing interest in learning low-dimensional vector representations of medical concepts from electronic health records (EHRs). While EHRs contain structured data such as diagnostic codes and laboratory tests, they also contain unstructured clinical notes, which provide more nuanced details on a patient's health status. In this work, we propose a method that jointly learns medical concept and word representations. In particular, we focus on capturing the relationship betweendoi:10.1109/bibm.2017.8217752 pmid:29375929 pmcid:PMC5783648 dblp:conf/bibm/BaiCEV17 fatcat:mjjw3b5dwzgtvf7stnlplgcqzq