与中风针的传统和现代方法相关的趋势和热点:图书识别和可视化分析
Chao-Yu Chuang1, Willy Chou2,3, Tsair-Wei Chien4
1Department of Chinese Medicine, Chi Mei Medical Center, Tainan, Taiwan.
Medicine
|December 5, 2023
概括
这项研究引入了一种新的集群分析技术,用于映射全球关于针治疗中风的研究. 它强调了关键的贡献者和趋势,倡导在未来的文献计量研究中使用追随者领先的集群算法 (FLCA).
科学领域:
- 整合和补充医学是整合和补充医学.
- 神经科学是一个神经科学.
- 图书统计学 图书统计学
背景情况:
- 针在中风治疗和康复方面越来越受欢迎.
- 在分析全球关于针治疗中风的研究的文献统计学研究中存在一个显著的差距.
- 现有的集群分析方法需要进一步探索精确性和全面的方法.
研究的目的:
- 引入和应用一种创新的集群分析技术,即追随者领先的集群算法 (FLCA).
- 评估过去十年中关于针治疗中风的全球出版物和研究趋势.
- 为了解决在文献计量研究中的集群分析方法的未被探索的方面.
主要方法:
- 2013-2022年出版物的图书统计分析来源于科学网络核心集合.
- 应用追随者领先的集群算法 (FLCA) 结合图书统计可视化工具.
- 评估贡献的国家/地区,机构,部门,作者,期刊和关键词.
主要成果:
- 从92个国家/地区确定了1050份出版物,截至2019年,这一数字显著增加.
- 北京 (中国),北京中医药大学和Lidian Chen被确定为主要贡献者.
- FLCA算法在共同词和作者协作分析中被证明是有效的,揭示了研究状态和趋势.
结论:
- 针治疗中风是一个重要的和不断扩大的研究领域.
- 研究人员和机构之间的全球合作对于推动这一领域的发展至关重要.
- 推使用追随者领先的集群算法 (FLCA) 来进行未来的图书统计集群分析.
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