  Natural language processing techniques are being applied to increasingly
diverse types of electronic health records, and can benefit from in-depth
understanding of the distinguishing characteristics of medical document types.
We present a method for characterizing the usage patterns of clinical concepts
among different document types, in order to capture semantic differences beyond
the lexical level. By training concept embeddings on clinical documents of
different types and measuring the differences in their nearest neighborhood
structures, we are able to measure divergences in concept usage while
correcting for noise in embedding learning. Experiments on the MIMIC-III corpus
demonstrate that our approach captures clinically-relevant differences in
concept usage and provides an intuitive way to explore semantic characteristics
of clinical document collections.
