Writing habits and telltale neighbors: analyzing clinical concept usage patterns with sublanguage embeddings
This work addresses the need for deeper semantic analysis in medical NLP to improve processing of diverse electronic health records, though it is incremental in applying embedding techniques to a specific domain.
The researchers tackled the problem of understanding semantic differences in clinical document types by analyzing concept usage patterns, and they developed a method using concept embeddings and nearest neighbor structures to capture clinically-relevant divergences, as demonstrated on the MIMIC-III corpus.
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.