Document Detail


Overcoming an obstacle in expanding a UMLS semantic type extent.
MedLine Citation:
PMID:  21925287     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
This paper strives to overcome a major problem encountered by a previous expansion methodology for discovering concepts highly likely to be missing a specific semantic type assignment in the UMLS. This methodology is the basis for an algorithm that presents the discovered concepts to a human auditor for review and possible correction. We analyzed the problem of the previous expansion methodology and discovered that it was due to an obstacle constituted by one or more concepts assigned the UMLS Semantic Network semantic type Classification. A new methodology was designed that bypasses such an obstacle without a combinatorial explosion in the number of concepts presented to the human auditor for review. The new expansion methodology with obstacle avoidance was tested with the semantic type Experimental Model of Disease and found over 500 concepts missed by the previous methodology that are in need of this semantic type assignment. Furthermore, other semantic types suffering from the same major problem were discovered, indicating that the methodology is of more general applicability. The algorithmic discovery of concepts that are likely missing a semantic type assignment is possible even in the face of obstacles, without an explosion in the number of processed concepts.
Authors:
Yan Chen; Huanying Gu; Yehoshua Perl; James Geller
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural     Date:  2011-09-09
Journal Detail:
Title:  Journal of biomedical informatics     Volume:  45     ISSN:  1532-0480     ISO Abbreviation:  J Biomed Inform     Publication Date:  2012 Feb 
Date Detail:
Created Date:  2012-02-03     Completed Date:  2012-04-20     Revised Date:  2013-06-27    
Medline Journal Info:
Nlm Unique ID:  100970413     Medline TA:  J Biomed Inform     Country:  United States    
Other Details:
Languages:  eng     Pagination:  61-70     Citation Subset:  IM    
Copyright Information:
Copyright © 2011 Elsevier Inc. All rights reserved.
Affiliation:
CIS Department, Borough of Manhattan Community College, CUNY, 199 Chamber Street, New York, NY 10007, USA. ychen@bmcc.cuny.edu
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MeSH Terms
Descriptor/Qualifier:
Algorithms*
Humans
Semantics*
Unified Medical Language System / standards*
Grant Support
ID/Acronym/Agency:
3R01LM008445-03S1/LM/NLM NIH HHS; 3R01LM008445-03S2/LM/NLM NIH HHS; R01 LM008445-01A2/LM/NLM NIH HHS; R01 LM008445-03S1/LM/NLM NIH HHS; R01 LM008445-03S2/LM/NLM NIH HHS; R01LM008445-01A2/LM/NLM NIH HHS
Comments/Corrections

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