Hanis Tengku Muhammad, Islam Md Asiful, Musa Kamarul Imran
Department of Community Medicine, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Kelantan, Malaysia.
Department of Haematology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Kelantan, Malaysia.
Curr Med Chem. 2022 Mar 4;29(8):1426-1435. doi: 10.2174/0929867328666211108110731.
Rapid advancement in computing technology and digital information leads to the possible use of machine learning on breast cancer.
This study aimed to evaluate the research output of the top 100 publications and further identify a research theme of breast cancer and machine-learning studies.
Databases of Scopus and Web of Science were used to extract the top 100 publications. These publications were filtered based on the total citation of each paper. Additionally, a bibliometric analysis was applied to the top 100 publications.
The top 100 publications were published between 1993 and 2019. The most productive author was Giger ML, and the top two institutions were the University of Chicago and the National University of Singapore. The most active countries were the USA, Germany, and China. Ten clusters were identified as both basic and specialised themes of breast cancer and machine learning.
Various countries demonstrated comparable interest in breast cancer and machine-learning research. A few Asian countries, such as China, India and Singapore, were listed in the top 10 countries based on the total citation. Additionally, the use of deep learning and breast imaging data was trending in the past 10 years in the field of breast cancer and machine-learning research.
计算技术和数字信息的快速发展使得机器学习在乳腺癌领域的应用成为可能。
本研究旨在评估前100篇出版物的研究成果，并进一步确定乳腺癌与机器学习研究的主题。
利用Scopus和Web of Science数据库提取前100篇出版物。这些出版物根据每篇论文的总被引次数进行筛选。此外，对前100篇出版物进行文献计量分析。
前100篇出版物发表于1993年至2019年之间。发文量最多的作者是吉格尔·ML，排名前两位的机构是芝加哥大学和新加坡国立大学。最活跃的国家是美国、德国和中国。确定了10个聚类作为乳腺癌与机器学习的基础主题和专门主题。
各国对乳腺癌与机器学习研究表现出相当的兴趣。一些亚洲国家，如中国、印度和新加坡，根据总被引次数位列前10。此外，在过去10年中，深度学习和乳腺影像数据的应用在乳腺癌与机器学习研究领域呈上升趋势。
