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

Leroy, Gondy and Chen, Hsinchun (2001) Meeting Medical Terminology Needs - the ontology-enhanced medical concept mapper. IEEE Transactions on Information Technology in Biomedicine 5(4):pp. 261-270.

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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.

EPrint Type:Journal Article (Paginated)
Keywords:National Science Digital Library, NSDL, Artificial Intelligence Lab, AI Lab, Ontologies, parsing, query expansion, semantic parsing, terminology mapping, UMLS.
Subjects:Artificial Intelligence
Medical Libraries
ID Code:537
Deposited On:29 October 2004
Alternative Locations:http://ai.bpa.arizona.edu/go/papers.html
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