Machine learning-based myocardial infarction bibliometric analysis

Ying Fang ^* Yuedi Wu ^* LIjuan GAo ^*

• Xiaoshan Hospital of Traditional Chinese Medicine, Hangzhou, China

Method: A total of 1036 publications were collected from the Web of Science core database. CiteSpace 6.3.R1, Bibliometrix, and VOSviewer were utilized to analyze bibliometric characteristics, determining the number of publications, countries, institutions, authors, keywords, and cited authors, documents, and journals in popular scientific fields. CiteSpace was used for temporal trend analysis, bibliometrix for quantitative country and institutional analysis, and VOSviewer for visualization of collaboration networks.: Since the emergence of research literature on medical imaging and machine learning (ML) in 2008, interest in this field has grown rapidly, particularly since the pivotal moment in 2016. The ML and MI domains, represented by China and the United States, have experienced swift development in research after 2015, albeit with the United States significantly outperforming China in research quality (as evidenced by the higher impact factors of journals and citation counts of publications from the US). Institutional collaborations have formed, notably between Harvard Medical School in the US and Capital Medical University in China, highlighting the need for enhanced cooperation among domestic and international institutions. In the realm of MI and ML research, cooperative teams led by figures such as Dey, Damini, and Berman, 2 Daniel S. in the US have emerged, indicating that Chinese scholars should strengthen their collaborations and focus on both qualitative and quantitative development. The overall direction of MI and ML research trends towards Medicine, Medical Sciences, Molecular Biology, and Genetics. Notably, publications in "Circulation" and "Computers in Biology and Medicine" from the United States hold prominent positions in this study.This paper presents a comprehensive exploration of the research hotspots, trends, and future directions in the field of MI and ML over the past two decades. The analysis reveals that deep learning is an emerging research direction in MI, with neural networks playing a crucial role in early diagnosis, risk assessment, and rehabilitation therapy.