一个现代的方法与追随者领先的集群算法可视化作者合作和文章主题在皮肤癌研究:一本图书法分析分析
Po-Chih Lai1, Willy Chou2,3, Tsair-Wei Chien4
1School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taiwan.
Medicine
|November 7, 2023
概括
这项研究分析了6,526篇皮肤癌文章,以确定研究合作和主题,突出了黑色素瘤作为主导主题. 两篇高度引用的文章被推进一步阅读.
科学领域:
- 图书统计学 图书统计学
- 皮肤病学 皮肤病学
- 在瘤学瘤学.
背景情况:
- 由于其转移潜力,皮肤癌 (SCs) 是一个重大的健康问题.
- 在SC研究中理解作者之间的基于国家的合作 (CBCA) 和文章主题是有限的.
- 需要一个集群算法来分类SC研究合作和主题.
研究的目的:
- 在皮肤癌研究中应用一个领先的追随者聚类算法来分类CBCA和文章主题.
- 确定和介绍在过去十年中发表的关于皮肤癌的具有影响力的文章.
主要方法:
- 从2013年到2022年,共分析了6526篇SC文章,使用了描述性,诊断性,预测性和处方性分析模型.
- 诊断分析被用来聚合基于国家的合作,并确定共同的文章主题.
- 关键字权重预测了文章的引用量,规范性分析突出了前100个引用量 (T100SC) 中的值得注意的文章.
主要成果:
- 美国,加利福尼亚大学旧金山分校,皮肤病学部门和作者安德烈亚斯·斯坦格是主要的贡献者.
- 美国皮肤病学会杂志发表的文章最多 (5.16%).
- 在前100个引用文章中,黑色素瘤是主要的主题 (92%),引用和关键字权重之间有很强的相关性 (P < .0001).
结论:
- 一个描述性,诊断性,预测性和处方性分析模型成功地确定了关于皮肤癌的两篇值得注意的文章.
- 这些突出文章,特别是关于黑色素瘤的文章,值得关注,并可能激发未来的文献计量研究.
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