Journal of Health & Medical Informatics

Jonathan R. Nebeker

Publications

• Case Report
  Characterizing Clinical Text and Sublanguage: A Case Study of the VA Clinical Notes
  Author(s): Qing T. Zeng, Doug Redd, Guy Divita, Samah Jarad, Cynthia Brandt and Jonathan R. NebekerQing T. Zeng, Doug Redd, Guy Divita, Samah Jarad, Cynthia Brandt and Jonathan R. Nebeker
  
  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 abl.. Read More»
  DOI: 10.4172/2157-7420.S3-001

  Abstract PDF