| Autor: |
Wen-Cai Liu, Meng-Pan Li, Hai-Yue Huang, Jing-Jie Min, Tao Liu, Ming-Xuan Li, Wei-Jie Liao, Hui Ying, Jun-Bo Tu |
| Předmět: |
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| Zdroj: |
Traditional Medicine Research; Jul2023, Vol. 8 Issue 7, p1-10, 10p |
| Abstrakt: |
Background: With the rapid development of the world's technology, the connection and integration between traditional medicine and modern machine learning technology are increasingly close. In this study, we aimed to analyze publications on machine learning in traditional medicine by using bibliometric methods and explore global trends in the field. Methods: Relevant research on machine learning in traditional medicine extracted from the Web of Science Core Collection database. Bibliometric analysis and visualization were performed using the Bibliometrix package in R software. Global trends, source journals, authorship, and thematic keywords analysis were performed in this study. Results: From 2012 to 2022, a total of 282 publications on machine learning in traditional medicine were identified and analyzed. The average annual growth rate of the publications was 13.35%. China had the largest contribution in this field (53.9%), followed by the United States (32.6%). IEEE Access had the largest number of published articles, with a total of 15 articles. Calvin Yu-Chian Chen, Xiao-juan Hu and Jue Wang were the main researchers in this field. Shanghai University of Traditional Chinese Medicine and University of California, San Francisco were the main research institutions. Conclusion: This study provides information on research trends in machine learning in traditional medicine to better understand research hotspots and future developments in this field. According to current global trends, the number of publications in this field will gradually increase. China currently dominated the field. Applied research of machine learning techniques may be the next hot topic in this field and deserves further attention. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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