NDLI: Exploring the effectiveness of medical entity recognition for clinical information retrieval

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Translating a trillion points of data into therapies, diagnostics, and new insights into disease

Exploring the effectiveness of medical entity recognition for clinical information retrieval

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Exploring the effectiveness of medical entity recognition for clinical information retrieval

Content Provider	ACM Digital Library
Author	Cogley, James Stokes, Nicola Carthy, Joe
Abstract	The growth of medical and clinical textual datasets has fostered research interests in methods for storing, retrieving and extracting of pertinent data. In more recent years, shared tasks and more comprehensive data sharing agreements have seen a further growth in the research area spanning Natural Language Processing (NLP) and Information Retrieval (IR) to aid the world of healthcare. Frequently NLP applications such as Medical Entity Recognition (MER), are motivated within the context of improving IR system performance. In this paper, we investigate the application of MER to a clinical retrieval system in the context of shared tasks in the respective areas. Namely, we aim to add structure to previously unstructured clinical reports and query sets. We evaluate the performance of MER on the query set, highlighting issues in constructing queries in a clinical setting. Further to this, we evaluate the performance of structuring queries on a retrieval dataset. We find that while structuring queries improves performance on complex queries that contain many term dependencies, there is a larger issue of linguistic variation found in clinical texts that must also be addressed.
Starting Page	3
Ending Page	4
Page Count	2
File Format	PDF
ISBN	9781450324199
DOI	10.1145/2512089.2512091
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-11-01
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Information extraction Medical information retrieval
Content Type	Text
Resource Type	Article

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