Abstract
With the rapid development of “Internet plus”, medical care has entered the era of big data. However, there is little research on medical big data (MBD) from the perspectives of bibliometrics and visualization. The substantive research on the basic aspects of MBD itself is also rare. This study aims to explore the current status of medical big data through visualization analysis on the journal papers related to MBD. We analyze a total of 988 references which were downloaded from the Science Citation Index Expanded and the Social Science Citation Index databases from Web of Science and the time span was defined as “all years”. The GraphPad Prism 5, VOSviewer and CiteSpace softwares are used for analysis. Many results concerning the annual trends, the top players in terms of journal and institute levels, the citations and H-index in terms of country level, the keywords distribution, the highly cited papers, the co-authorship status and the most influential journals and authors are presented in this paper. This study points out the development status and trends on MBD. It can help people in the medical profession to get comprehensive understanding on the state of the art of MBD. It also has reference values for the research and application of the MBD visualization methods.
Highlights
With the rapid development of “Internet plus”, almost all industry and business data shows explosive growth in recent years [1]
This paper aims to adopt the bibliometric analysis and visualization on medical big data (MBD) to explore the characteristics of this area
The co-authorship analysis and the co-citation analysis are displayed in Sections 3.3 and 3.4, respectively
Summary
With the rapid development of “Internet plus”, almost all industry and business data shows explosive growth in recent years [1]. Big data is a common buzzword in business and research community, referring to great mass of digital data collected from various sources [2]. Big data has the characteristics of the “5V” [3]: Variety: the data is from a variety of sources, and the types and formats of data are becoming richer. It has broken through the category of structured data previously defined, including semi-structured and unstructured data. Volume: the volume of data is huge, including the amount of data that is collected, stored and calculated. Velocity: it requires fast processing and fast access to high value information for different types of data, which is fundamentally different from those traditional data mining techniques
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