Challenges in Understanding Clinical Notes: Why NLP Engines Fall Short and Where Background Knowledge Can Help

TitleChallenges in Understanding Clinical Notes: Why NLP Engines Fall Short and Where Background Knowledge Can Help
Publication TypeConference Proceedings
Year of Publication2013
AuthorsSujan Perera, Amit Sheth, Krishnaprasad Thirunarayan, Suhas Nair, Neil Shah
Conference NameInternational Workshop on Data management & Analytics for healthcaRE (DARE) at ACM Conference of Information and Knowledge Management (CIKM)
Pagination21-26
Date Published11/2013
Conference LocationBurlingame, California
Keywordselectronic medical records, Knowledge Base, NLP shortcomings
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.

DOI10.1145/2512410.2512427
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