Bibliometric analysis on cardiovascular disease treated by traditional Chinese medicines based on big data. Issue 3 (3rd May 2020)

• Record Type: Journal Article
• Authors: Liu, Junnan; Zhai, Xing; Liao, Xianfu
• Is Part Of: International journal of parallel, emergent and distributed systems. Volume 35:Issue 3(2020)
• Page Start: 323
• Page End: 339
• Publication Date: 2020-05-03
• Subjects: Big data -- data analysis -- cardiovascular diseases -- bibliometrics -- TCM; Parallel computers -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Computer algorithms -- Periodicals; 004.35
• DOI: 10.1080/17445760.2019.1606912
• Languages: English
• ISSNs: 1744-5760

Abstract

Abstract : In recent years, with the ageing of the population, environmental degradation and dietary abnormalities, the incidence of global cardiovascular disease has increased sharply, its serious impact on physical and mental health of humans is an important factor. Chinese medicine has a long history and significant efficacy in the treatment of cardiovascular disease. Based on the bibliometric method in the context of big data, this paper searched and screened the relevant literatures of TCM in the treatment of cardiovascular diseases included by SCI from 2004 to 2016 and analysed them from the aspects of annual change, regional distribution, institutional distribution, journal source, research field, highly cited literatures, high-frequency keywords and so on. The analysis results show that the total amount of literature in the treatment of cardiovascular diseases by traditional Chinese medicine is on the rise, mainly focusing on the application of traditional Chinese medicine and Chinese herbal compound, acupuncture and moxibustion, and taiji and other traditional Chinese medicine regimen. The research results of this paper provide a direction reference for TCM researchers to better understand the current research status of TCM in the treatment of cardiovascular diseases under the background of big data and for further research. GRAPHICAL ABSTRACT: