Enhancement of medical named entity recognition using graph-based features

Keretna, Sara, Lim, Chee Peng and Creighton, Douglas 2015, Enhancement of medical named entity recognition using graph-based features, in SMC 2015 : Big Data Analytics for Human-Centric Systems. Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics, IEEE, Piscataway, N.J., pp. 1895-1900, doi: 10.1109/SMC.2015.331.

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Title Enhancement of medical named entity recognition using graph-based features
Author(s) Keretna, Sara
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Creighton, DouglasORCID iD for Creighton, Douglas orcid.org/0000-0002-9217-1231
Conference name IEEE International Conference on Systems, Man, and Cybernetics (2015 : Hong Kong, China)
Conference location Hong Kong, China
Conference dates 9-12 Oct. 2015
Title of proceedings SMC 2015 : Big Data Analytics for Human-Centric Systems. Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics
Publication date 2015
Start page 1895
End page 1900
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Science & Technology
Technology
Computer Science, Cybernetics
Computer Science, Information Systems
Computer Science, Theory & Methods
Computer Science
Machine learning
conditional random field
biomedical named entity recognition
information extraction
medical text mining
TEXT
IDENTIFICATION
CONSTRUCTION
CLASSIFIER
Summary Named Entity Recognition (NER) is a crucial step in text mining. This paper proposes a new graph-based technique for representing unstructured medical text. The new representation is used to extract discriminative features that are able to enhance the NER performance. To evaluate the usefulness of the proposed graph-based technique, the i2b2 medication challenge data set is used. Specifically, the 'treatment' named entities are extracted for evaluation using six different classifiers. The F-measure results of five classifiers are enhanced, with an average improvement of up to 26% in performance.
ISBN 9781479986965
ISSN 1062-922X
Language eng
DOI 10.1109/SMC.2015.331
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082494

Document type: Conference Paper
Collections: Centre for Intelligent Systems Research
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