Writing habits and telltale neighbors: analyzing clinical concept usage patterns with sublanguage embeddings

Denis Newman-Griffis, Eric Fosler-Lussier


Abstract
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.
Anthology ID:
D19-6218
Volume:
Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)
Month:
November
Year:
2019
Address:
Hong Kong
Venues:
EMNLP | Louhi | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
146–156
Language:
URL:
https://www.aclweb.org/anthology/D19-6218
DOI:
10.18653/v1/D19-6218
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PDF:
http://aclanthology.lst.uni-saarland.de/D19-6218.pdf