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First published April 25, 2007 as JAMIA PrePrint; doi:10.1197/jamia.M2314
Journal of the American Medical Informatics Association 2007;14(4):467-477
© 2007 American Medical Informatics Association

A more recent version of this article appeared on July 1, 2007
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Submitted on October 25, 2006
Accepted on April 9, 2007

Semantic classification of biomedical concepts using distributional similarity

Jung-Wei Fan MS1* and Carol Friedman PhD1

Affiliation of the authors: 1 Department of Biomedical Informatics, Columbia University in New York, NY

* To whom correspondence should be addressed.

Objective To develop an automated, high-throughput and reproducible method for reclassifying and validating ontological concepts for natural language processing applications.

Design We developed a distributional similarity approach to classify the Unified Medical Language System (UMLS) concepts. Classification models were built for seven broad biomedically relevant semantic classes created by grouping subsets of the UMLS semantic types. We used contextual features based on syntactic properties obtained from two different large corpora and used {alpha}-skew divergence as the similarity measure.

Measurements The testing sets were automatically generated based on the changes by the National Library of Medicine to the semantic classification of concepts from the UMLS 2005AA to the 2006AA release. Error rates were calculated and a misclassification analysis was performed.

Results The estimated lowest error rates were 0.198 and 0.116 when considering the correct classification to be covered by our top prediction and top 2 predictions respectively.

Conclusion The results demonstrated that the distributional similarity approach can recommend high level semantic classification suitable for use in natural language processing.




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J.-W. Fan and C. Friedman
Semantic reclassification of the UMLS concepts
Bioinformatics, September 1, 2008; 24(17): 1971 - 1973.
[Abstract] [Full Text] [PDF]


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