Leveraging medical taxonomies to improve knowledge management within online communities of practice: The knowledge maps system.

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  • Author(s): Stewart SA;Stewart SA; Abidi SS; Abidi SS
  • Source:
    Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2017 May; Vol. 143, pp. 121-127. Date of Electronic Publication: 2017 Mar 03.
  • Publication Type:
    Journal Article
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Elsevier Scientific Publishers Country of Publication: Ireland NLM ID: 8506513 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-7565 (Electronic) Linking ISSN: 01692607 NLM ISO Abbreviation: Comput Methods Programs Biomed Subsets: MEDLINE
    • Publication Information:
      Publication: Limerick : Elsevier Scientific Publishers
      Original Publication: Amsterdam : Elsevier Science Publishers, c1984-
    • Subject Terms:
      Knowledge Management*; Translational Research, Biomedical/*methods; Algorithms ; Communication ; Health Personnel ; Humans ; Internet ; Medical Subject Headings ; Medicine ; Natural Language Processing ; Pain ; Pediatrics ; Semantics ; Software ; Surgeons
    • Abstract:
      Background and Objective: Online communities of practice contain a wealth of information, stored in the free text of shared communications between community members. The Knowledge Maps (KMaps) system is designed to facilitate Knowledge Translation in online communities through multi-level analyses of the shared messages of these communications.
      Methods: Using state-of-the-art semantic mapping technologies (Metamap) the contents of the messages shared within an online community are mapped to terms from the MeSH medical lexicon, providing a multi-level topic-specific summary of the knowledge being shared within the community. Using the inherent hierarchical structure of the lexicon important insights can be found within the community.
      Results: The KMaps system was applied to two medical mailing lists, the PPML (archives from 2009-02 to 2013-02) and SURGINET (archives from 2012-01 to 2013-04), identifying 27,924 and 50,597 medical terms respectively. KMaps identified content areas where both communities found interest, specifically around Diseases, 22% and 24% of the total terms, while also identifying field-specific areas that were more popular: SURGINET expressed an interest in Anatomy (14% vs 4%) while the PPML was more interested in Drugs (19% vs 9%). At the level of the individual KMaps identified 6 PPML users and 9 SURGINET users that had noticeably more contributions to the community than their peers, and investigated their personal areas of interest.
      Conclusion: The KMaps system provides valuable insights into the structure of both communities, identifying topics of interest/shared content areas and defining content-profiles for individual community members. The system provides a valuable addition to the online KT process.
      (Copyright © 2017 Elsevier B.V. All rights reserved.)
    • Contributed Indexing:
      Keywords: Knowledge management; Medical taxonomies; Natural language processing; Online communities; Semantic mapping
    • Publication Date:
      Date Created: 20170411 Date Completed: 20170926 Latest Revision: 20211204
    • Publication Date:
      20231215
    • Accession Number:
      10.1016/j.cmpb.2017.03.003
    • Accession Number:
      28391809