Meeting Medical Terminology Needs - the ontology-enhanced medical concept mapper

Persistent Link:
http://hdl.handle.net/10150/105242
Title:
Meeting Medical Terminology Needs - the ontology-enhanced medical concept mapper
Author:
Leroy, Gondy; Chen, Hsinchun
Citation:
Meeting Medical Terminology Needs - the ontology-enhanced medical concept mapper 2001-12, 5(4):261-270 IEEE Transactions on Information Technology in Biomedicine
Publisher:
IEEE
Journal:
IEEE Transactions on Information Technology in Biomedicine
Issue Date:
Dec-2001
Description:
Artificial Intelligence Lab, Department of MIS, University of Arizona
URI:
http://hdl.handle.net/10150/105242
Submitted date:
2004-10-29
Abstract:
This paper describes the development and testing of the Medical Concept Mapper, a tool designed to facilitate access to online medical information sources by providing users with appropriate medical search terms for their personal queries. Our system is valuable for patients whose knowledge of medical vocabularies is inadequate to find the desired information, and for medical experts who search for information outside their field of expertise. The Medical Concept Mapper maps synonyms and semantically related concepts to a user's query. The system is unique because it integrates our natural language processing tool, i.e., the Arizona (AZ) Noun Phraser, with human-created ontologies, the Unified Medical Language System (UMLS) and WordNet, and our computer generated Concept Space, into one system. Our unique contribution results from combining the UMLS Semantic Net with Concept Space in our deep semantic parsing (DSP) algorithm. This algorithm establishes a medical query context based on the UMLS Semantic Net, which allows Concept Space terms to be filtered so as to isolate related terms relevant to the query. We performed two user studies in which Medical Concept Mapper terms were compared against human experts' terms. We conclude that the AZ Noun Phraser is well suited to extract medical phrases from user queries, that WordNet is not well suited to provide strictly medical synonyms, that the UMLS Metathesaurus is well suited to provide medical synonyms, and that Concept Space is well suited to provide related medical terms, especially when these terms are limited by our DSP algorithm.
Type:
Journal Article (Paginated)
Language:
en
Keywords:
Artificial Intelligence; Medical Libraries
Local subject classification:
National Science Digital Library; NSDL; Artificial Intelligence lab; AI lab; Ontologies; Parsing; Query expansion; Semantic parsing; Terminology mapping; UMLS

Full metadata record

DC FieldValue Language
dc.contributor.authorLeroy, Gondyen_US
dc.contributor.authorChen, Hsinchunen_US
dc.date.accessioned2004-10-29T00:00:01Z-
dc.date.available2010-06-18T23:22:06Z-
dc.date.issued2001-12en_US
dc.date.submitted2004-10-29en_US
dc.identifier.citationMeeting Medical Terminology Needs - the ontology-enhanced medical concept mapper 2001-12, 5(4):261-270 IEEE Transactions on Information Technology in Biomedicineen_US
dc.identifier.urihttp://hdl.handle.net/10150/105242-
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractThis paper describes the development and testing of the Medical Concept Mapper, a tool designed to facilitate access to online medical information sources by providing users with appropriate medical search terms for their personal queries. Our system is valuable for patients whose knowledge of medical vocabularies is inadequate to find the desired information, and for medical experts who search for information outside their field of expertise. The Medical Concept Mapper maps synonyms and semantically related concepts to a user's query. The system is unique because it integrates our natural language processing tool, i.e., the Arizona (AZ) Noun Phraser, with human-created ontologies, the Unified Medical Language System (UMLS) and WordNet, and our computer generated Concept Space, into one system. Our unique contribution results from combining the UMLS Semantic Net with Concept Space in our deep semantic parsing (DSP) algorithm. This algorithm establishes a medical query context based on the UMLS Semantic Net, which allows Concept Space terms to be filtered so as to isolate related terms relevant to the query. We performed two user studies in which Medical Concept Mapper terms were compared against human experts' terms. We conclude that the AZ Noun Phraser is well suited to extract medical phrases from user queries, that WordNet is not well suited to provide strictly medical synonyms, that the UMLS Metathesaurus is well suited to provide medical synonyms, and that Concept Space is well suited to provide related medical terms, especially when these terms are limited by our DSP algorithm.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectMedical Librariesen_US
dc.subject.otherNational Science Digital Libraryen_US
dc.subject.otherNSDLen_US
dc.subject.otherArtificial Intelligence laben_US
dc.subject.otherAI laben_US
dc.subject.otherOntologiesen_US
dc.subject.otherParsingen_US
dc.subject.otherQuery expansionen_US
dc.subject.otherSemantic parsingen_US
dc.subject.otherTerminology mappingen_US
dc.subject.otherUMLSen_US
dc.titleMeeting Medical Terminology Needs - the ontology-enhanced medical concept mapperen_US
dc.typeJournal Article (Paginated)en_US
dc.identifier.journalIEEE Transactions on Information Technology in Biomedicineen_US
All Items in UA Campus Repository are protected by copyright, with all rights reserved, unless otherwise indicated.