Chen Ying, Xiao Xiang, He Qing, Yao Rui-Qi, Zhang Gao-Yu, Fan Jia-Rong, Xue Chong-Xiang, Huang Li
Beijing University of Chinese Medicine, Beijing, 100029, China.
Department of Integrative Cardiology, China-Japan Friendship Hospital, Beijing, 100029, China.
Heliyon. 2024 Feb 1;10(3):e25318. doi: 10.1016/j.heliyon.2024.e25318. eCollection 2024 Feb 15.
To review studies on digital medicine in cardiovascular diseases (CVD), discuss its development process, knowledge structure and research hotspots, and provide a perspective for researchers in this field.
The relevant literature in recent 20 years (January 2004 to October 2022) were retrieved from the Web of Science Core Collection (WoSCC). CiteSpace was used to demonstrate our knowledge of keywords, co-references and speculative frontiers. VOSviewer was used to chart the contributions of authors, institutions and countries and incorporates their link strength into the table.
A total of 5265 English articles in set timespan were included. The number of publications increased steadily annually. The United States (US) produced the highest number of publications, followed by England. Most publications were from Harvard Medicine School, followed by Massachusetts General Hospital and Brigham Women's Hospital. The most authoritative academic journal was . Noseworthy PA may have the highest influence in this intersected field with the highest number of citations and total link strength. The utilization of wearable mobile devices in the context of CVD, encompassing the identification of risk factors, diagnosis and prevention of diseases, as well as early intervention and remote management of diseases, has been widely acknowledged as a knowledge base and an area of current interest. To investigate the impact of various digital medicine interventions on chronic care and assess their clinical effectiveness, examine the potential of machine learning (ML) in delivering clinical care for atrial fibrillation (AF) and identifying early disease risk factors, as well as explore the development of disease prediction models using neural networks (NNs), ML and unsupervised learning in CVD prognosis, may emerge as future trends and areas of focus.
Recently, there has been a significant surge of interest in the investigation of digital medicine in CVD. This initial bibliometric study offers a comprehensive analysis of the research landscape pertaining to digital medicine in CVD, thereby furnishing related scholars with a dependable reference to facilitate further progress in this domain.
回顾心血管疾病（CVD）数字医学的研究，探讨其发展历程、知识结构和研究热点，为该领域的研究人员提供一个视角。
从科学网核心合集（WoSCC）中检索近20年（2004年1月至2022年10月）的相关文献。使用CiteSpace展示关键词、共被引文献和前沿热点的知识图谱。使用VOSviewer绘制作者、机构和国家的贡献，并将其链接强度纳入表格。
在设定的时间范围内共纳入5265篇英文文章。每年的出版物数量稳步增加。美国的出版物数量最多，其次是英国。大多数出版物来自哈佛医学院，其次是麻省总医院和布莱根妇女医院。最具权威性的学术期刊是 。Noseworthy PA在这个交叉领域可能具有最高的影响力，其被引次数和总链接强度最高。在CVD背景下可穿戴移动设备的应用，包括风险因素识别、疾病诊断和预防以及疾病的早期干预和远程管理，已被广泛认可为一个知识库和当前关注的领域。研究各种数字医学干预对慢性病护理的影响并评估其临床效果，研究机器学习（ML）在房颤（AF）临床护理和早期疾病风险因素识别中的潜力，以及探索使用神经网络（NNs）、ML和无监督学习在CVD预后中疾病预测模型的发展，可能会成为未来的趋势和重点关注领域。
最近，对CVD数字医学的研究兴趣显著激增。这项初步的文献计量学研究对CVD数字医学的研究格局进行了全面分析，从而为相关学者提供了可靠的参考，以促进该领域的进一步发展。
