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Visualization of health‐subject analysis based on query term co‐occurrences

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  • Jin Zhang
  • Dietmar Wolfram
  • Peiling Wang
  • Yi Hong
  • Rick Gillis

Abstract

A multidimensional‐scaling approach is used to analyze frequently used medical‐topic terms in queries submitted to a Web‐based consumer health information system. Based on a year‐long transaction log file, five medical focus keywords (stomach, hip, stroke, depression, and cholesterol) and their co‐occurring query terms are analyzed. An overlap‐coefficient similarity measure and a conversion measure are used to calculate the proximity of terms to one another based on their co‐occurrences in queries. The impact of the dimensionality of the visual configuration, the cutoff point of term co‐occurrence for inclusion in the analysis, and the Minkowski metric power k on the stress value are discussed. A visual clustering of groups of terms based on the proximity within each focus‐keyword group is also conducted. Term distributions within each visual configuration are characterized and are compared with formal medical vocabulary. This investigation reveals that there are significant differences between consumer health query‐term usage and more formal medical terminology used by medical professionals when describing the same medical subject. Future directions are discussed.

Suggested Citation

  • Jin Zhang & Dietmar Wolfram & Peiling Wang & Yi Hong & Rick Gillis, 2008. "Visualization of health‐subject analysis based on query term co‐occurrences," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(12), pages 1933-1947, October.
    • Handle: RePEc:bla:jamist:v:59:y:2008:i:12:p:1933-1947
    DOI: 10.1002/asi.20911
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    Cited by:

    1. Jin Zhang & Shanshan Zhai & Hongxia Liu & Jennifer Ann Stevenson, 2016. "Social network analysis on a topic‐based navigation guidance system in a public health portal," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(5), pages 1068-1088, May.

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