[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122206-en":3,"doc-seo-122206-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122206,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine learning-based myocardial infarction bibliometric analysis - Systematic Review","This systematic review examines research trends in machine learning (ML) for myocardial infarction (MI) from 2008 to 2024 to surface emerging themes, hotspots, and future directions. Using 1,036 Web of Science Core Collection publications, the study applies CiteSpace for temporal trends, Bibliometrix for quantitative country and institutional analysis, and VOSviewer for collaboration networks. Results show rapid growth since 2008, a notable acceleration around 2016, and strong contributions led by China and the United States, with deep learning emerging as a key direction for diagnosis, risk assessment, and rehabilitation.","OPEN ACCESS  \nEDITED BY  \nMd. Mohaimenul Islam,  \nThe Ohio State University, United States  \nREVIEWED BY  \nHosna Salmani,  \nIran University of Medical Sciences, Iran Jian Wang,  \nUniversity of Leicester, United Kingdom  \n*CORRESPONDENCE  \nYing Fang  \n [13750882155@163.com](13750882155@163.com)  \nRECEIVED 07 August 2024  \nACCEPTED 17 January 2025  \nPUBLISHED 06 February 2025  \nCITATION  \nFang Y, Wu Y and Gao L (2025) Machine learning-based myocardial infarction bibliometric analysis.  \nFront. Med. 12:1477351 .  \ndoi: 10.3389/fmed.2025.1477351  \nCOPYRIGHT  \n© 2025 Fang, Wu and Gao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nTYPE Systematic Review PUBLISHED 06 February 2025 DOI 10.3389/fmed.2025.1477351  \nMachine learning-based myocardial infarction bibliometric analysis  \nYing Fang*, Yuedi Wu and Lijuan Gao  \nXiaoshan District Hospital of Traditional Chinese Medicine, Hangzhou, Zhejiang Province, China  \nPurpose: This study analyzed the research trends in machine learning (ML) pertaining to myocardial infarction (MI) from 2008 to 2024, aiming to identify emerging trends and hotspots in the field, providing insights into the future directions of research and development in ML for MI. Additionally, it compared the contributions of various countries, authors, and agencies to the field of ML research focused on MI.  \nMethod: A total of 1,036 publications were collected from the Web of Science Core Collection 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.  \nResults: 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 United States) . Institutional collaborationshave formed, notably between Harvard Medical School in the United States 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, Daniel [S. in](S. in) the United States 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 toward Medicine, Medical Sciences, Molecular Biology, and Genetics. In particular, publications in “Circulation” and “Computers in Biology and Medicine”from the United States hold prominent positions in this study.  \nConclusion: 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.  \nKEYWORDS  \nmachine learning, myocardial infarc","cbCaiuu5j2WhCfzX","https://ap.wps.com/l/cbCaiuu5j2WhCfzX","pdf",3013779,1,15,"English","en",105,"# Introduction\n## Background and clinical need\n## Challenges in early detection and management\n## Role of machine learning in MI\n# Methods\n## Data collection and bibliometric tools\n# Results\n## Publication trends and key regions\n## Collaboration networks and research directions\n# Conclusion\n## Hotspots and future directions","[{\"question\":\"What time period and dataset were used to analyze MI-related machine learning research?\",\"answer\":\"The study analyzed 1,036 publications from 2008 to 2024 using the Web of Science Core Collection database.\"},{\"question\":\"Which bibliometric tools were used and for what purposes?\",\"answer\":\"CiteSpace was used for temporal trend analysis, Bibliometrix for quantitative country and institutional analysis, and VOSviewer to visualize collaboration networks.\"},{\"question\":\"What major research trend emerged for MI and machine learning?\",\"answer\":\"Deep learning emerged as an important research direction, supported by neural networks’ roles in early diagnosis, risk assessment, and rehabilitation therapy.\"}]","Machine learning-based myocardial infarction bibliometric analysis - 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