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Research status and trend analysis of global biomedical text mining studies in recent 10 years

Author

Listed:
  • Xing Zhai

    (Beijing University of Chinese Medicine)

  • Zhihong Li

    (Beijing University of Chinese Medicine)

  • Kuo Gao

    (Beijing University of Chinese Medicine)

  • Youliang Huang

    (Beijing University of Chinese Medicine)

  • Lin Lin

    (HuBei University)

  • Le Wang

    (Beijing University of Chinese Medicine)

Abstract

Objective In recent years, with the abrupt growth of the amount of biomedical literature, a lot of implicit laws and new knowledge were buried in the vast literature, while the text mining technology, if applied in the biomedical field, can integrate and analyze massive biomedical literature data, obtaining valuable information to improve people’s understanding of biomedical phenomena. This paper mainly discussed the research status of text mining technology applied in the biomedical field in recent 10 years in order to provide a reference for further studies of other researchers. Methods Biomedical text mining literature included in SCI from 2004 to 2013 were retrieved and filtered and then were analyzed from the perspectives of annual changes, regional distribution, research institutions, journals sources, research fields, keywords and so on. Results The total amount of global biomedical text mining literature is on the rise, among which literature relevant to named entity recognition, entity relation extraction, text categorization, text clustering, abbreviations extraction and co-occurrence analysis take up a large percentage; studies in USA and the UK are in the leading position. Conclusion Compared with other much more mature research topics, the application of text mining technology in biomedicine is still a relatively new research field worldwide, while with the constantly improving awareness of this field and deepening researches in this area, a number of core research areas, core research institutes and core research fields have been formed in this field. Therefore, further researches of this field will inject new vitality in the development of biomedicine.

Suggested Citation

  • Xing Zhai & Zhihong Li & Kuo Gao & Youliang Huang & Lin Lin & Le Wang, 2015. "Research status and trend analysis of global biomedical text mining studies in recent 10 years," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(1), pages 509-523, October.
    • Handle: RePEc:spr:scient:v:105:y:2015:i:1:d:10.1007_s11192-015-1700-9
    DOI: 10.1007/s11192-015-1700-9
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    References listed on IDEAS

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    1. Heiko Müller & Francesco Mancuso, 2008. "Identification and Analysis of Co-Occurrence Networks with NetCutter," PLOS ONE, Public Library of Science, vol. 3(9), pages 1-16, September.
    2. Ling-Li Li & Guohua Ding & Nan Feng & Ming-Huang Wang & Yuh-Shan Ho, 2009. "Global stem cell research trend: Bibliometric analysis as a tool for mapping of trends from 1991 to 2006," Scientometrics, Springer;Akadémiai Kiadó, vol. 80(1), pages 39-58, July.
    3. Shaodong Xie & Jing Zhang & Yuh-Shan Ho, 2008. "Assessment of world aerosol research trends by bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 77(1), pages 113-130, October.
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    Cited by:

    1. Mauricio Marrone & Sascha Lemke & Lutz M. Kolbe, 2022. "Entity linking systems for literature reviews," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 3857-3878, July.

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