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Presentation
Challenges in Understanding Clinical Notes: Why NLP Engines Fall Short and Where Background Knowledge Can Help
Proceedings of the 2013 International Workshop on Data Management & Analytics for Healthcare
  • Sujan Perera, Wright State University - Main Campus
  • Amit P. Sheth, Wright State University - Main Campus
  • Krishnaprasad Thirunarayan, Wright State University - Main Campus
  • Suhas Nair
  • Neil Shah
Document Type
Conference Proceeding
Publication Date
11-1-2013
Catalog Record
Catalog Record
Abstract
Understanding of Electronic Medical Records(EMRs) plays a crucial role in improving healthcare outcomes. However, the unstructured nature of EMRs poses several technical challenges for structured information extraction from clinical notes leading to automatic analysis. Natural Language Processing(NLP) techniques developed to process EMRs are effective for variety of tasks, they often fail to preserve the semantics of original information expressed in EMRs, particularly in complex scenarios. This paper illustrates the complexity of the problems involved and deals with conflicts created due to the shortcomings of NLP techniques and demonstrates where domain specific knowledge bases can come to rescue in resolving conflicts that can significantly improve the semantic annotation and structured information extraction. We discuss various insights gained from our study on real world dataset.
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Presented at the International Workshop on Data Management & Analytics for Healthcare, San Francisco, CA, November 1, 2013.

DOI
10.1145/2512410.2512427
Citation Information
Sujan Perera, Amit P. Sheth, Krishnaprasad Thirunarayan, Suhas Nair, et al.. "Challenges in Understanding Clinical Notes: Why NLP Engines Fall Short and Where Background Knowledge Can Help" Proceedings of the 2013 International Workshop on Data Management & Analytics for Healthcare (2013) p. 21 - 26 ISSN: 9781450324250
Available at: http://works.bepress.com/tk_prasad/70/