YEARS

2007-2009

AUTHORS

Carol Friedman

TITLE

Semantic and Machine Learning Methods for Mining Connections in the UMLS

ABSTRACT

DESCRIPTION (provided by applicant): The Unified Medical Language System (UMLS) is an invaluable resource for the biomedical community. One of the intended uses of the UMLS Metathesaurus is to support the translation of terms from a source terminology into terms in a target terminology. It is evident from the research literature on the UMLS that users generally need to perform more broader types of "translations" that involve finding terms with closest meaning to source term (mapping), finding terms that are related to source term and can serve as proxy for various functions (e.g. information retrieval, knowledge discovery) or finding target terms that satisfy some structural or semantic constraint (e.g. information theoretic distance). The methods for finding such "translations" or connections between terms in Meta (other than the case of one-to-one synonymy) are not at all clear. Previous attempts to exploit such connections have depended on either manual selection of relevant connections, or problem-specific algorithms that use expert knowledge about the relative suitability of various inter-concept relationships. We believe that machine learning techniques offer automated, generalizable approaches that are appropriate for use with the UMLS, given the large set of potential connections and the need for a problem-independent approach. We hypothesize that learning strategies that exploit the relational features, scale free properties and probabilistic dependencies of connections in the UMLS will identify meaningful inter-term relationships and that a combined approach will perform better across different problem domains when compared to any of the approaches in isolation. We will evaluate the proposed learning algorithms with training connections from a variety of problem domains in biomedicine. We will disseminate the successful algorithms via the UMLS Knowledge Source API toolkit for mining and visualizing the connections. We believe that the UMLS provides a unique fertile ground to develop novel semantic relational mining methods and advance our understanding of mining large biomedical concept graphs.

FUNDED PUBLICATIONS

  • A network-theoretic approach for decompositional translation across Open Biological Ontologies.
  • Proton Pump Inhibitors and Risk for Recurrent Clostridium difficile Infection Among Inpatients
  • Proton pump inhibitors and risk for recurrent Clostridium difficile infection among inpatients.
  • Using semantic and structural properties of the Unified Medical Language System to discover potential terminological relationships.
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    22 TRIPLES      17 PREDICATES      23 URIs      9 LITERALS

    Subject Predicate Object
    1 grants:72b5e32f85dfc1d1920604d35a04df8e sg:abstract DESCRIPTION (provided by applicant): The Unified Medical Language System (UMLS) is an invaluable resource for the biomedical community. One of the intended uses of the UMLS Metathesaurus is to support the translation of terms from a source terminology into terms in a target terminology. It is evident from the research literature on the UMLS that users generally need to perform more broader types of "translations" that involve finding terms with closest meaning to source term (mapping), finding terms that are related to source term and can serve as proxy for various functions (e.g. information retrieval, knowledge discovery) or finding target terms that satisfy some structural or semantic constraint (e.g. information theoretic distance). The methods for finding such "translations" or connections between terms in Meta (other than the case of one-to-one synonymy) are not at all clear. Previous attempts to exploit such connections have depended on either manual selection of relevant connections, or problem-specific algorithms that use expert knowledge about the relative suitability of various inter-concept relationships. We believe that machine learning techniques offer automated, generalizable approaches that are appropriate for use with the UMLS, given the large set of potential connections and the need for a problem-independent approach. We hypothesize that learning strategies that exploit the relational features, scale free properties and probabilistic dependencies of connections in the UMLS will identify meaningful inter-term relationships and that a combined approach will perform better across different problem domains when compared to any of the approaches in isolation. We will evaluate the proposed learning algorithms with training connections from a variety of problem domains in biomedicine. We will disseminate the successful algorithms via the UMLS Knowledge Source API toolkit for mining and visualizing the connections. We believe that the UMLS provides a unique fertile ground to develop novel semantic relational mining methods and advance our understanding of mining large biomedical concept graphs.
    2 sg:endYear 2009
    3 sg:fundingAmount 334328.0
    4 sg:fundingCurrency USD
    5 sg:hasContribution contributions:be126dada73e71264a76dd684d1c648f
    6 sg:hasFieldOfResearchCode anzsrc-for:08
    7 anzsrc-for:0801
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    9 sg:hasFundedPublication articles:2ca2a451722759f7cc2af81563a39aec
    10 articles:57651c151df168022093e47ebdc57290
    11 articles:7a7f8a1e7db2f45bbc40ce312eab0fb1
    12 articles:e49f287951a411eaa9c8976593621c89
    13 sg:hasFundingOrganization grid-institutes:grid.280285.5
    14 sg:hasRecipientOrganization grid-institutes:grid.413734.6
    15 sg:language English
    16 sg:license http://scigraph.springernature.com/explorer/license/
    17 sg:scigraphId 72b5e32f85dfc1d1920604d35a04df8e
    18 sg:startYear 2007
    19 sg:title Semantic and Machine Learning Methods for Mining Connections in the UMLS
    20 sg:webpage http://projectreporter.nih.gov/project_info_description.cfm?aid=7498449
    21 rdf:type sg:Grant
    22 rdfs:label Grant: Semantic and Machine Learning Methods for Mining Connections in the UMLS
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