COVID-19 研究中涉及机器学习方法的文献计量研究。

COVID-19 studies involving machine learning methods: A bibliometric study.

机构信息

Koç University, School of Medicine, Department of Biostatistics, Istanbul, Turkey.

Istanbul University, Institute of Child Health, Department of Social Pediatrics, Istanbul, Türkiye.

出版信息

Medicine (Baltimore). 2023 Oct 27;102(43):e35564. doi: 10.1097/MD.0000000000035564.

BACKGROUND: Machine learning (ML) and artificial intelligence (AI) techniques are gaining popularity as effective tools for coronavirus disease of 2019 (COVID-19) research. These strategies can be used in diagnosis, prognosis, therapy, and public health management. Bibliometric analysis quantifies the quality and impact of scholarly publications. ML in COVID-19 research is the focus of this bibliometric analysis. METHODS: A comprehensive literature study found ML-based COVID-19 research. Web of Science (WoS) was used for the study. The searches included "machine learning," "artificial intelligence," and COVID-19. To find all relevant studies, 2 reviewers searched independently. The network visualization was analyzed using VOSviewer 1.6.19. RESULTS: In the WoS Core, the average citation count was 13.6 ± 41.3. The main research areas were computer science, engineering, and science and technology. According to document count, Tao Huang wrote 14 studies, Fadi Al-Turjman wrote 11, and Imran Ashraf wrote 11. The US, China, and India produced the most studies and citations. The most prolific research institutions were Harvard Medical School, Huazhong University of Science and Technology, and King Abdulaziz University. In contrast, Nankai University, Oxford, and Imperial College London were the most mentioned organizations, reflecting their significant research contributions. First, "Covid-19" appeared 1983 times, followed by "machine learning" and "deep learning." The US Department of Health and Human Services funded this topic most heavily. Huang Tao, Feng Kaiyan, and Ashraf Imran pioneered bibliographic coupling. CONCLUSION: This study provides useful insights for academics and clinicians studying COVID-19 using ML. Through bibliometric data analysis, scholars can learn about highly recognized and productive authors and countries, as well as the publications with the most citations and keywords. New data and methodologies from the pandemic are expected to advance ML and AI modeling. It is crucial to recognize that these studies will pioneer this subject.

背景：机器学习（ML）和人工智能（AI）技术作为 2019 年冠状病毒病（COVID-19）研究的有效工具越来越受欢迎。这些策略可用于诊断、预后、治疗和公共卫生管理。文献计量分析量化了学术出版物的质量和影响力。本文献计量分析的重点是 COVID-19 研究中的 ML。

方法：全面的文献研究发现了基于 ML 的 COVID-19 研究。本研究使用了 Web of Science（WoS）。搜索包括“机器学习”、“人工智能”和 COVID-19。为了找到所有相关研究，2 位审稿人独立搜索。使用 VOSviewer 1.6.19 分析网络可视化。

结果：在 WoS 核心中，平均引文数为 13.6±41.3。主要研究领域是计算机科学、工程和科技。根据文献数量，Tao Huang 撰写了 14 项研究，Fadi Al-Turjman 撰写了 11 项，Imran Ashraf 撰写了 11 项。美国、中国和印度发表的研究和引文最多。最有成效的研究机构是哈佛医学院、华中科技大学和阿卜杜勒阿齐兹国王大学。相比之下，南开大学、牛津大学和伦敦帝国理工学院是被引用最多的组织，反映了它们在这一领域的重要研究贡献。首先，“Covid-19”出现了 1983 次，其次是“机器学习”和“深度学习”。美国卫生与公众服务部对此主题的资助最多。Tao Huang、Feng Kaiyan 和 Imran Ashraf 率先进行了文献共引分析。

结论：本研究为使用 ML 研究 COVID-19 的学者和临床医生提供了有用的见解。通过文献计量数据分析，学者可以了解到被高度认可和富有成效的作者和国家，以及引用最多和关键词最多的出版物。预计大流行期间的新数据和方法将推动 ML 和 AI 建模的发展。必须认识到，这些研究将开创这一主题。