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    HelpfulMed: Intelligent Searching for Medical Information over the Internet

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    Author
    Chen, Hsinchun
    Lally, Ann M.
    Zhu, Bin
    Chau, Michael
    Issue Date
    2003-05
    Submitted date
    2004-08-16
    Keywords
    National Science Digital Library
    NSDL
    Artificial intelligence lab
    AI lab
    HelpfulMed
    Web Mining
    Medical Libraries
    
    Metadata
    Show full item record
    Citation
    HelpfulMed: Intelligent Searching for Medical Information over the Internet 2003-05, 54(7):683-694 Journal of the American Society for Information Science & Technology
    Publisher
    Wiley Periodicals, Inc
    Journal
    Journal of the American Society for Information Science & Technology
    Description
    Artificial Intelligence Lab, Department of MIS, University of Arizona
    URI
    http://hdl.handle.net/10150/105202
    Abstract
    Medical professionals and researchers need information from reputable sources to accomplish their work. Unfortunately, the Web has a large number of documents that are irrelevant to their work, even those documents that purport to be â medically-related.â This paper describes an architecture designed to integrate advanced searching and indexing algorithms, an automatic thesaurus, or â concept space,â and Kohonen-based Self-Organizing Map (SOM) technologies to provide searchers with finegrained results. Initial results indicate that these systems provide complementary retrieval functionalities. HelpfulMed not only allows users to search Web pages and other online databases, but also allows them to build searches through the use of an automatic thesaurus and browse a graphical display of medical-related topics. Evaluation results for each of the different components are included. Our spidering algorithm outperformed both breadth-first search and PageRank spiders on a test collection of 100,000 Web pages. The automatically generated thesaurus performed as well as both MeSH and UMLSâ systems which require human mediation for currency. Lastly, a variant of the Kohonen SOM was comparable to MeSH terms in perceived cluster precision and significantly better at perceived cluster recall.
    Type
    Journal Article (Paginated)
    Language
    en
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