NDLI: Challenges in understanding clinical notes: why NLP engines fall short and where background knowledge can help

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Challenges in understanding clinical notes: why NLP engines fall short and where background knowledge can help

Content Provider	ACM Digital Library
Author	Perera, Sujan Nair, Suhas Shah, Neil Sheth, Amit Thirunarayan, Krishnaprasad
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.
Starting Page	21
Ending Page	26
Page Count	6
File Format	PDF
ISBN	9781450324250
DOI	10.1145/2512410.2512427
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-11-01
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Negation detection Knowledge base Natural language processing
Content Type	Text
Resource Type	Article

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