东南亚龋齿研究中的机器学习趋势:文献计量分析的启示

Faizul Hasan, Hendrik Setia Budi, Lia Taurussia Yuliana, Mokh. Sujarwadi
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引用次数: 0

摘要

背景 龋齿是一种常见的慢性口腔疾病,是一个严重的公共卫生问题。通过分析大型数据集,机器学习显示出解决这一问题的潜力。本研究通过文献计量分析,探讨东南亚地区与机器学习在龋齿管理中的应用相关的新兴课题、合作、主要作者和研究趋势。方法 使用 Scopus 数据库进行综合筛选,以获得相关研究,涵盖从开始到 2024 年 7 月的出版物。我们对收集到的数据采用了文献计量学方法,包括合著网络、年度出版趋势、机构和国家合作关系、关键词共现分析和引文分析。为了探索可视化和网络分析,我们使用了 R 软件包中的 VOSviewer 和 Bibliometrix 等工具。结果 最终的文献计量分析包括 246 篇论文。我们发现,马来西亚以 59 篇论文位居榜首,其次是印度尼西亚(37 篇)和泰国(29 篇)。马来西亚的多国发表率(MCP)最高,为 0.407。包括马来西亚理科大学在内的顶尖院校发表了 39 篇文章,清迈大学(36 篇)紧随其后,新加坡国立大学(30 篇)成为领头羊。使用 VOSviewer 进行的合著分析显示了六个不同的群组。共有 1220 名学者为这些出版物做出了贡献。包括 "人类 "和 "龋齿 "在内的前 10 个关键词表明了研究热点。结论 我们发现在东南亚有越来越多的证据表明机器学习应用于解决龋齿问题。文献计量分析突出了主要作者、合作网络和新兴课题,揭示了自2014年以来的研究趋势。这项研究强调了文献计量分析在解决这一公共卫生问题方面的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Trends of machine learning for dental caries research in Southeast Asia: insights from a bibliometric analysis
Background Dental caries is a common chronic oral disease, posing a serious public health issue. By analyzing large datasets, machine learning shows potential in addressing this problem. This study employs bibliometric analysis to explore emerging topics, collaborations, key authors, and research trends in Southeast Asia related to the application of machine learning in dental caries management. Methods A comprehensive selection using the Scopus database to obtain relevant research, covering publications from inception to July 2024 was done. We employed the Bibliometric approaches, including co-authorship networks, yearly publishing trends, institutional and national partnerships, keyword co-occurrence analysis, and citation analysis, for the collected data. To explore the visualization and network analysis, we employed the tools such as VOSviewer and Bibliometrix in R package. Results The final bibliometric analysis included 246 papers. We found that Malaysia became the top contributor with 59 publications, followed by Indonesia (37) and Thailand (29). Malaysia had the highest Multiple Country Publications (MCP) ratio at 0.407. Top institutions including the Universiti Sains Malaysia led with 39 articles, followed by Chiang Mai University (36) and the National University of Singapore (30) became the leader. Co-authorship analysis using VOSviewer revealed six distinct clusters. A total of 1220 scholars contributed to these publications. The top 10 keywords, including ‘human’ and ‘dental caries,’ indicated research hotspots. Conclusion We found growing evidence of machine learning applications to address dental caries in Southeast Asia. The bibliometric analysis highlights key authors, collaborative networks, and emerging topics, revealing research trends since 2014. This study underscores the importance of bibliometric analysis in tackling this public health issue.
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