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.

本文介绍了一种基于概念嵌入的医疗文档类型的语义差异刻画方法，通过训练概念嵌入，能够在消除嵌入噪声的同时，度量概念使用上的差异，从而实现对文档集合的语义特征的探索。