Abstract
This paper aims at discovering the recent research status of diabetes in China through a data-driven bibliometrics and knowledge mapping analysis method on diabetes-related literature. With the basis of 24,561 publication documents from CNKI during 2007–2016, the quantitative analysis are conducted in three aspects: (1) descriptive statistical method for acquiring literature distribution characteristics; (2) hierarchical clustering, k-means clustering analysis, and multidimensional scaling analysis based on a keyword co-occurrence matrix for discovering research hotspots; and (3) network analysis for revealing cooperation relationships among authors and affiliations. The result shows some findings about the recent diabetes research in China. It also demonstrates the close cooperation of diabetes research among productive authors and affiliations through network generation and visualization.
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Acknowledgements
The work was supported by National Natural Science Foundation of China (No. 61772146 & 61403088), Frontier and Key Technology Innovation Special Grant of Guangdong Province (No. 2014B010118005), Public Interest Research and Capability Building Grant of Guangdong Province (No. 2014A020221039), and Innovative School Project in Higher Education of Guangdong Province (No. YQ2015062).
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Chen, X., Weng, H., Hao, T. (2017). A Data-Driven Approach for Discovering the Recent Research Status of Diabetes in China. In: Siuly, S., et al. Health Information Science. HIS 2017. Lecture Notes in Computer Science(), vol 10594. Springer, Cham. https://doi.org/10.1007/978-3-319-69182-4_10
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DOI: https://doi.org/10.1007/978-3-319-69182-4_10
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