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Updated: Jul 11, 2025

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Published on: October 24, 2019

过去20年来牛皮病研究的全面概述:基于机器学习的文献识别分析

Chenyang Yu^1,2, Yingzhao Huang³, Wei Yan¹

¹Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China.

Frontiers in immunology

|November 13, 2023

概括

这份图书统计分析揭示了牛皮研究的关键趋势,强调分子病变和皮肤炎症作为主要热点. 还注意到对指甲牛皮和相关并发症的注意力增加.

科学领域:

皮肤病学皮肤病学
图书统计学图书统计学
计算生物学计算生物学

背景情况:

越来越多的牛皮研究带来了信息管理的挑战.
由于缺乏元数据处理工具,缺乏全面的文献计量分析.

研究的目的:

评估研究趋势,并通过使用文献计量和机器学习分析识别牛皮的当前热点.
提供关于牛皮研究演变的宏观视角.

主要方法:

从PubMed中使用MeSH术语"牛皮"提取了28178篇出版物 (2003-2022年).
应用隐性迪里克莱特分配 (LDA) 给摘要,用于主题识别和趋势分析.
利用卢温算法绘制主题关系并识别研究集群.

主要成果:

确定了最大的贡献国 (美国,中国,意大利) 和频繁的MeSH术语 ("疾病严重性指数"",治疗结果"",皮肤病原体").
LDA揭示了关键的研究领域:"分子致病","临床试验"和"皮肤炎症".
突出突出出现的热点,如"指甲牛皮"",流行病学研究"和并发症 ("心血管并发症"",牛皮关节炎"",肥胖"",心理障碍").

结论:

牛皮的研究正在迅速推进,目前的焦点是"分子发病","皮肤炎症"和"临床试验".
"皮肤炎症"和"生物病原体"之间的联系表明,基础研究有效地转化为临床研究.
对"指甲牛皮","流行病学研究"和并发症的兴趣日益增长,表明研究范围正在扩大.

关键词:

圣经计量资料 (Bibliometric bibliometric bibliometric) 是指参考书目中的资料.隐藏的迪里克莱特分配 (LDA) 算法机器学习是机器学习.自然语言处理 (NLP)牛皮是一种牛皮.

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