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

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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.

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Acknowledgments

This research is supported by Young Talent Project of Beijing (No. YETP0821) and Research Project for Practice Development of National TCM Clinical Research Bases.

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    Authors and Affiliations

    1. Beijing University of Chinese Medicine, Beijing, 100029, China

      Xing Zhai, Kuo Gao & Youliang Huang

    2. Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, 100700, China

      Zhihong Li

    3. Knowledge and Action College, HuBei University, Wuhan, 430011, China

      Lin Lin

    4. Dongfang Hospital, Beijing University of Chinese Medicine, Beijing, 100078, China

      Le Wang

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    1. Xing Zhai
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    Correspondence to Le Wang.

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    Xing Zhai, Zhihong Li and Kuo Gao have contributed equally to this work.

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    Zhai, X., Li, Z., Gao, K. et al. Research status and trend analysis of global biomedical text mining studies in recent 10 years. Scientometrics 105, 509–523 (2015). https://doi.org/10.1007/s11192-015-1700-9

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