使用机器学习方法对肥胖研究的研究:从2004年到2023年的文献计量和可视化分析
Xiao-Wei Gong1,2, Si-Yu Bai2, En-Ze Lei2
1Wuhan Hospital of Traditional Chinese Medicine, Wuhan, China.
Medicine
|September 10, 2024
概括
肥胖研究中的机器学习正在迅速增长,美国的出版物领先. 关键主题包括深度学习和肠道微生物群,未来的研究重点是肥胖症.
科学领域:
- 图书计量和健康信息学
- 肥胖问题研究研究
- 机器学习应用 机器学习应用
背景情况:
- 肥胖是一个重大的全球健康挑战,需要先进的分析方法.
- 机器学习 (ML) 为肥胖查,诊断和分析提供了巨大的潜力.
- 在肥胖研究中缺乏对ML应用的系统评估.
研究的目的:
- 在肥胖研究中对机器学习的出版物进行定量检查,可视化和分析.
- 识别这一跨学科领域的趋势,有影响力的作品和新兴主题.
- 为研究人员和临床医生提供文献统计概述.
主要方法:
- 从2004年到2023年对出版物的图书统计分析.
- 数据来源于科学网络核心集合 (英语文章和评论).
- 这些分析工具包括VOSviewer,CiteSpace和Excel.
主要成果:
- 关于用于肥胖研究的机器学习的出版物的指数增长.
- 美国主导着出版量;Leo Breiman被认为是一个有影响力的作者.
- 关键的研究领域包括深度学习,支持载体机器,肠道微生物群和基因组分析.
结论:
- 图书统计分析揭示了ML驱动的肥胖研究中的发展模式和内在关系.
- 确定当前的热点和未来的研究方向,包括与糖尿病视网膜病变和COVID-19的联系.
- 为早期发现和个性化治疗肥胖提供了有价值的学术参考.
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