Characterizing Clinical Text and Sublanguage: A Case Study of the VA Clinical Notes
Received Date: Nov 22, 2011 / Accepted Date: Dec 22, 2011 / Published Date: Dec 26, 2011
Objective: To characterize text and sublanguage in medical records to better address challenges within Natural Language Processing (NLP) tasks such as information extraction, word sense disambiguation, information retrieval, and text summarization. The text and sublanguage analysis is needed to scale up the NLP development for large and diverse free-text clinical data sets. Design: This is a quantitative descriptive study which analyzes the text and sublanguage characteristics of a very large Veteran Affairs (VA) clinical note corpus (569 million notes) to guide the customization of natural language processing (NLP) of VA notes. Methods: We randomly sampled 100,000 notes from the top 100 most frequently appearing document types. We examined surface features and used those features to identify sublanguage groups using unsupervised clustering. Results: Using the text features we are able to characterize each of the 100 document types and identify 16 distinct sublanguage groups. The identified sublanguages reflect different clinical domains and types of encounters within the sample corpus. We also found much variance within each of the document types. Such characteristics will facilitate the tuning and crafting of NLP tools. Conclusion: Using a diverse and large sample of clinical text, we were able to show there are a relatively large number of sublanguages and variance both within and between document types. These findings will guide NLP development to create more customizable and generalizable solutions across medical domains and sublanguages.
Citation: Zeng QT, Redd D, Divita G, Jarad S, Brandt C, et al. (2011) Characterizing Clinical Text and Sublanguage: A Case Study of the VA Clinical Notes. J Health Med Informat S3:001. Doi: 10.4172/2157-7420.S3-001
Copyright: © 2011 Zeng QT, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Select your language of interest to view the total content in your interested language
Share This Article
- Total views: 13204
- [From(publication date): 6-2013 - Dec 08, 2019]
- Breakdown by view type
- HTML page views: 9364
- PDF downloads: 3840