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Research Trend Visualization by MeSH Terms from PubMed

Author

Listed:
  • Heyoung Yang

    (Kore Institute of Science and Technology Information, 66, Hoegi-ro, Dongdaemun-gu, Seoul 02456, Korea)

  • Hyuck Jai Lee

    (Kore Institute of Science and Technology Information, 66, Hoegi-ro, Dongdaemun-gu, Seoul 02456, Korea)

Abstract

Motivation : PubMed is a primary source of biomedical information comprising search tool function and the biomedical literature from MEDLINE which is the US National Library of Medicine premier bibliographic database, life science journals and online books. Complimentary tools to PubMed have been developed to help the users search for literature and acquire knowledge. However, these tools are insufficient to overcome the difficulties of the users due to the proliferation of biomedical literature. A new method is needed for searching the knowledge in biomedical field. Methods : A new method is proposed in this study for visualizing the recent research trends based on the retrieved documents corresponding to a search query given by the user. The Medical Subject Headings (MeSH) are used as the primary analytical element. MeSH terms are extracted from the literature and the correlations between them are calculated. A MeSH network, called MeSH Net, is generated as the final result based on the Pathfinder Network algorithm. Results : A case study for the verification of proposed method was carried out on a research area defined by the search query (immunotherapy and cancer and “tumor microenvironment”). The MeSH Net generated by the method is in good agreement with the actual research activities in the research area (immunotherapy). Conclusion : A prototype application generating MeSH Net was developed. The application, which could be used as a “guide map for travelers”, allows the users to quickly and easily acquire the knowledge of research trends. Combination of PubMed and MeSH Net is expected to be an effective complementary system for the researchers in biomedical field experiencing difficulties with search and information analysis.

Suggested Citation

  • Heyoung Yang & Hyuck Jai Lee, 2018. "Research Trend Visualization by MeSH Terms from PubMed," IJERPH, MDPI, vol. 15(6), pages 1-14, May.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:6:p:1113-:d:149705
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    References listed on IDEAS

    as
    1. Chaomei Chen, 2006. "CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 359-377, February.
    2. Arnaud Quirin & Oscar Cordón & Vicente P. Guerrero‐Bote & Benjamín Vargas‐Quesada & Felix Moya‐Anegón, 2008. "A quick MST‐based algorithm to obtain Pathfinder networks (∞, n − 1)," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(12), pages 1912-1924, October.
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    4. Sternitzke, Christian & Bartkowski, Adam & Schramm, Reinhard, 2008. "Visualizing patent statistics by means of social network analysis tools," World Patent Information, Elsevier, vol. 30(2), pages 115-131, June.
    5. Steven H. Strogatz, 2001. "Exploring complex networks," Nature, Nature, vol. 410(6825), pages 268-276, March.
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    Cited by:

    1. Han, Linlin & Shan, Zidan & Lei, Ming & Long, Suwan(Cheng), 2024. "A comparative study of international and Chinese digitization from the perspective of mapping knowledge domains," International Review of Economics & Finance, Elsevier, vol. 89(PB), pages 93-113.
    2. Ekaterina V. Ilgisonis & Mikhail A. Pyatnitskiy & Svetlana N. Tarbeeva & Artem A. Aldushin & Elena A. Ponomarenko, 2022. "How to catch trends using MeSH terms analysis?," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1953-1967, April.

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