Wu Mingfen, Yu Kefu, Zhao Zhigang, Zhu Bin
Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China.
Heliyon. 2024 Jan 12;10(2):e24230. doi: 10.1016/j.heliyon.2024.e24230. eCollection 2024 Jan 30.
Machine learning (ML) models have been widely applied in stroke prediction, diagnosis, treatment, and prognosis assessment. We aimed to conduct a comprehensive scientometrics analysis of studies related to ML in stroke and reveal its current status, knowledge structure, and global trends.
All documents related to ML in stroke were retrieved from the Web of Science database on March 15, 2023. We refined the documents by including only original articles and reviews in the English language. The literature published over the past decade was imported into scientometrics software for influence detection and collaborative network analysis.
2389 related publications were included. The annual publication outputs demonstrated explosive growth, with an average growth rate of 63.99 %. Among the 90 countries/regions involved, the United States (729 articles) and China (636 articles) were the most productive countries. Frontiers in Neurology was the most prolific journal with 94 articles. 234 highly cited articles, each with more than 31 citations, were detected. Keyword analysis revealed a total of 5333 keywords, with a predominant focus on the application of ML models in the early diagnosis, classification, and prediction of "acute ischemic stroke" and "atrial fibrillation-related stroke". The keyword "classification" had the first and longest burst, spanning from 2013 to 2018. 'Upport vector machine' got the strongest burst strength with 6.2. Keywords such as 'mechanical thrombectomy', 'expression', and 'prognosis' experienced bursts in 2022 and have continued to be prominent.
The applications of ML in stroke are increasingly diverse and extensive, with researchers showing growing interest over the past decade. However, the clinical application of ML in stroke is still in its early stages, and several limitations and challenges need to be addressed for its widespread adoption in clinical practice.
机器学习（ML）模型已广泛应用于中风的预测、诊断、治疗和预后评估。我们旨在对与中风中ML相关的研究进行全面的科学计量学分析，揭示其当前状况、知识结构和全球趋势。
2023年3月15日从科学网数据库中检索所有与中风中ML相关的文献。我们通过仅纳入英文原创文章和综述来对文献进行筛选。将过去十年发表的文献导入科学计量学软件进行影响力检测和合作网络分析。
共纳入2389篇相关出版物。年度出版物产出呈现爆发式增长，平均增长率为63.99%。在涉及的90个国家/地区中，美国（729篇文章）和中国（636篇文章）是发文量最多的国家。《神经学前沿》是发文量最多的期刊，有94篇文章。检测到234篇高被引文章，每篇被引次数超过31次。关键词分析共揭示了5333个关键词，主要集中在ML模型在“急性缺血性中风”和“心房颤动相关性中风”的早期诊断、分类和预测中的应用。关键词“分类”的爆发最早且持续时间最长，从2013年到2018年。“支持向量机”的爆发强度最强，为6.2。“机械取栓”“表达”和“预后”等关键词在2022年出现爆发，并持续受到关注。
ML在中风中的应用越来越多样化和广泛，在过去十年中研究人员的兴趣不断增加。然而，ML在中风中的临床应用仍处于早期阶段，在临床实践中广泛应用还需要解决一些限制和挑战。
