Medical Research Center, Jazan University, Jazan, Kingdom of Saudi Arabia.
Pharmaceutical Care Administration (Jeddah Second Health Cluster), Ministry of Health, Saudi Arabia.
Curr Probl Cardiol. 2024 Oct;49(10):102754. doi: 10.1016/j.cpcardiol.2024.102754. Epub 2024 Jul 28.
The objective of this study is to conduct a comprehensive bibliometric analysis to elucidate the landscape of machine learning applications in ischemia research.
The analysis can be divided in three sections: part 1 scrutinizes articles and reviews with "ischemia" in their titles, while part 2 further narrows the focus to publications containing both "ischemia" and "machine learning" in their titles. Additionally, part 3 delves into the examination of the top 50 most cited papers, exploring their thematic focus and co-word dynamics.
The findings reveal a significant increase in publications over the years, with notable trends identified through detailed analysis. The growth in publication counts over time, the leading contributors, institutions, geographical distribution of research output and journals are numerically presented for part 1 and part 2. For the top 50 most cited papers the dynamics of co-words, which offer a nuanced understanding of thematic trends and emerging concepts, are presented. Based on the number of citations the top 10 authors were selected, and later for each, total number of publications, h-index, g-index and m-index are provided. Additionally, figures depicting the co-authorship network among authors, departments, and countries involved in the top 50 cited papers may enrich our comprehension of collaborative networks in ischemia research.
This comprehensive bibliometric analysis provides valuable insights into the evolving landscape of machine learning applications in ischemia research.
本研究旨在进行全面的文献计量分析，阐明机器学习在缺血研究中的应用全景。
分析可分为三个部分：第 1 部分仔细审查标题中含有“缺血”的文章和综述，而第 2 部分则进一步将重点缩小到标题中同时含有“缺血”和“机器学习”的出版物。此外，第 3 部分深入研究了前 50 篇被引最多的论文，探讨了它们的主题重点和共词动态。
研究结果显示，近年来出版物数量显著增加，通过详细分析确定了显著趋势。随着时间的推移，出版物数量的增长、主要贡献者、机构、研究成果的地理分布和期刊在第 1 部分和第 2 部分中以数字形式呈现。对于前 50 篇被引最多的论文，共词动态提供了对主题趋势和新兴概念的细致理解。根据引用次数选择了前 10 位作者，然后为每位作者提供了总出版物数量、h 指数、g 指数和 m 指数。此外，还提供了涉及前 50 篇被引最多论文的作者、部门和国家之间合著网络的图表，以丰富我们对缺血研究中合作网络的理解。
这项全面的文献计量分析为理解机器学习在缺血研究中的应用不断发展的全景提供了有价值的见解。
