对心力衰竭相关疾病的100篇最多引用的文章进行了图书统计分析,并与机器学习相结合
Xuyuan Kuang1,2, Zihao Zhong3, Wei Liang3
1Department of Hyperbaric Oxygen, Xiangya Hospital, Changsha, China.
Frontiers in cardiovascular medicine
|June 12, 2023
概括
这种图书统计分析揭示了用于心力衰竭研究的机器学习趋势. 支持向量机器是最受欢迎的方法,为未来的心脏病学AI发展提供了洞察力.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 图书统计学 图书统计学
背景情况:
- 机器学习 (ML) 在心力衰竭 (HF) 研究中的应用正在迅速扩大.
- 需要进行全面的文献计量分析,以了解高频 ML 的风景.
研究的目的:
- 对有关心力衰竭的ML出版物进行动态和纵向的文献分析.
- 分析ML在心力衰竭相关疾病中的应用.
主要方法:
- 在科学网上选相关文章 (1985-2023年).
- 参考文献指标和直观的数据分析,用于引用最多的文章.
- 用于分析相关性,影响和并发网络的VOSViewer.
主要成果:
- 包括2,392篇论文,支持矢量机器是最常用的ML方法.
- 分析显示了共同作者,引用和关键词共同出现的模式.
- 哈佛大学和加利福尼亚大学在机构中领先于出版物.
结论:
- 提供了人工智能在心力衰竭研究中的全面概述.
- 帮助机构和研究人员了解人工智能前景,并制定研究计划.
- 协助确定人工智能技术在心力衰竭中的优势,风险和影响.
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