Cross-Cultural Communication
Knowledge Mapping Analysis on Text Mining Research of Medicine Related Fields in Different Regions
Abstract
In order to trace the trend of text mining research in medicine related fields through the massive literature, we analyzed the bibliographical reference data of relevant literature in the WOS database with methods of bibliometric and knowledge mapping. We concluded the research state from aspects of time sequence, core authors and institutions, regional and disciplinary distribution; and summarized the research hot points and frontiers through knowledge mapping analysis by using assistant tool CitespaceⅢ. Our analysis indicates that text mining research in medicine related fields appears a steady-state growth trend and state of multidisciplinary integration; and text mining technology has been widely applied to biomedical field such as named entity recognition task, construction and automatic annotation of gene or protein relating corpus, and biomedical event extraction based on various text mining tools. Besides, the research in recent years turns to the EHR information extraction and knowledge discovery, drug knowledge mining and social media mining, etc. In conclusion, it’s worth applying text mining technology to explore medical information, especially clinical information or other aspects more extensively and
thoroughly.
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Chen, C. M. (2004). Searching for intellectual turning points: Progressive knowledge domain visualization. Proceedings of the National Academy of Sciences of the United States of America (PNAS), 101(1), 5303-5310.
Chen, C. M., Chen, Y., & Horowitz, M., et al. (2009). Towards an explantory and computitional theory of scientific discovery. Journal of Informetrics, 3(3), 191-209.
Chen, C. M. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359-377.
Cohen, R., & Elhadad, M., et al. (2013). Redundancy in electronic health record corpora: Analysis, impact on text mining performance and mitigation strategies. BMC Bioinformatics, 14, 10. doi:10.1186/1471-2105-14-10
Feng, S. J., Zhao, W. L., & Li, Z. (2012). Research hotspots and evolution path of clinical pathway. Research on Science and Technology Management, 32(10), 62-65.
Fleuren, W. W. M., & Alkema, W. (2015). Application of text mining in the biomedical domain. Methods, 74, 97-106.
Harpaz, R., & Callahan, A., et al. (2014). Text mining for adverse drug events: The promise, challenges, and state of the art. Drug Safety, 37(10), 777-790.
Hearst, M. (1999). Untangling text data mining. Proc. Assoc. Comput. Linguist., 37, 3-10.
Jonnalagadda, S. R., Adupa, A. K., Garg, R. P., Corona-Cox, J., & Shah, S. J. (2017). Text mining of the electronic health record: An information extraction approach for automated identification and subphenotyping of HFPEF patients for clinical trials. Journal of Cardiovascular Translational Research, 1-9.
Kleinberg, J. (2002). Bursty and hierarchical structure in streams (p.91, 101). Proceedings of the 8th ACMSIGKDD Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada: ACM Press.
Marshall, S. A., Yang, C. C., Ping, Q., Zhao, M., Avis, N. E., & Ip, E. H. (2015). Symptom clusters in women with breast cancer: An analysis of data from social media and a research study. Quality of Life Research an International Journal of Quality of Life Aspects of Treatment Care & Rehabilitation, 25(3), 1-11.
Papanikolaou, N., et al. (2015) Protein–protein interaction predictions using text mining methods. Methods, 74, 47-53.
Pletscher-Frankild, S., et al. (2015). DISEASES: Text mining and data integration of disease–gene associations. Methods, 74, 83-89.
Price, D. D. (1965). Networks of scientific papers. Science, 149, 510-515.
Small, H. (1980). Co-citation context analysis and the structure of paradigms. Journal of Ocumentation, 36(3), 183-196.
Tikk, D., Thomas, P., Palaga, P., Hakenberg, J., & Leser, U. (2010). A comprehensive benchmark of kernel methods to extract protein-protein interactions from literature. PLoS Comput Biol, 6(7), e10.
Uzuner, O., et al. (2008). Identifying patient smoking status from medical discharge records. J Am Med Inform Assoc, 15(1), 14-24.
Zheng, L., Wang, Y., & Hao, S., et al. (2016). Web-based real-time case finding for the population health management of patients with diabetes mellitus: A prospective validation of the natural language processing-based algorithm with statewide electronic medical records. Jmir Medical Informatics, 4(4), e37.
DOI: http://dx.doi.org/10.3968/10006
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