社交媒体药物警戒热点和趋势的CiteSpace分析OA
Hotspots and Trends of Social Media-Based Pharmacovigilance:A CiteSpace Analysis
目的 探讨社交媒体药物警戒的研究现状、热点及发展趋势,为我国社交媒体药物警戒的发展提供参考.方法 检索Web of Science核心合集数据库自建库起至 2025 年 8 月 1 日社交媒体药物警戒的相关研究,采用CiteSpace 6.4.R2 软件对纳入文献的年发文量、作者、研究机构、国家/地区、关键词、参考文献等进行可视化分析.结果 共纳入 256 篇文献,年发文量整体呈缓慢起步,快速增长,再到波动调整的发展趋势.共涉及 341 位作者,其中Lin Hongfei(9 篇)和Bousquet Cedric(9 篇)发文量最多,其次为Sarker Abeed(7 篇)、Wang Jian(7 篇)、Yang Zhihao(6 篇);共涉及 267 家研究机构,其中Institut National de la Santé et de la Recherche Médi-cale(Inserm)(法国国家健康与医学研究院,12 篇)发文量最多,其次为Dalian University of Technology(大连理工大学,11 篇)、Assis-tance Publique Hôpitaux Paris(APHP,巴黎公立医院集团,8 篇)、Sorbonne Universite(索邦大学,8 篇);共涉及 41 个国家/地区,其中美国(117 篇)发文量最多,其次为中国(34 篇)、英国(32 篇).社交媒体驱动的药物警戒、药品不良反应(ADR)/药品不良事件的智能监测与分析、自然语言处理与药物安全文本挖掘是目前该领域的研究热点,人工智能驱动的疫苗不良反应监测、自动化ADR信号监测与提取技术、患者视角的真实世界证据与沟通是未来的研究趋势,社交媒体药物警戒研究的发展呈明显的技术驱动和公共卫生需求导向特征.共被引频次排名前 5 的文献集中发表于 2014 年至 2017 年,该阶段学者均聚焦于从社交媒体非结构化文本中识别ADR.结论 社交媒体药物警戒已逐步从边缘交叉领域发展成为药物安全监测体系中不可或缺的组成部分,我国国家药品监督管理局、高校等可协同互联网平台,借鉴并发展符合我国国情的社交媒体药物警戒挖掘技术,推动我国药物警戒体系升级.
Objective To investigate the research status,hotspots,and development trends of social media-based pharmacovigilance,and to provide a reference for the development of social media-based pharmacovigilance in China.Methods Studies related to social media-based pharmacovigilance in the Web of Science Core Collection database were searched from its inception to August 1,2025.CiteSpace 6.4.R2 software was used to visually analyze the annual publication volume,authors,research institutions,countries/regions,keywords,and references of the included studies.Results A total of 256 studies were included,and the overall annual publication volume showed a slow start,rapid growth,and then a trend of fluctuation adjustment.A total of 341 authors were involved,with Lin Hongfei(nine articles)and Bousquet Cedric(nine articles)having the highest publication volume,followed by Sarker Abeed(seven articles),Wang Jian(seven articles),and Yang Zhihao(six articles).A total of 267 research institutions were involved,with Institution National de la Santé et de la Recherche Médicale(Inserm)(12 articles)having the highest publication volume,followed by the Dalian University of Technology(11 articles),Assistance Publique Hôpitaux Paris(APHP,eight articles),and Sorbonne Université(eight articles).A total of 41 countries/regions were involved,with the United States of America(117 articles)having the highest publication volume,followed by China(34 articles)and the United Kingdom(32 articles).Social media-driven pharmacovigilance,intelligent detection and analysis of adverse drug reactions(ADR)/adverse drug events(ADE),natural language processing and drug safety text mining were currently the research hotspots in this field.Artificial intelligence(AI)-driven vaccine adverse reaction monitoring,automated ADR signal detection and extraction technologies,as well as patient-centered real-world evidence and communication were future research trends.The development of social media-based pharmacovigilance research exhibited distinct characteristics of technology-driven advancement and public health demand orientation.The top five most cited papers were published from 2014 and 2017,during which scholars focused on identifying ADR from unstructured social media texts.Conclusion Social media-based pharmacovigilance has gradually evolved from an interdisciplinary field to an indispensable component of the drug safety monitoring system.The National Medical Products Administration,universities and other institutions in China can collaborate with Internet platforms,draw on and develop social media-based pharmacovigilance mining technologies that are in line with national conditions in China,and promote the upgrading of the pharmacovigilance system in China.
黄乐怡;魏芬芳;李丽敏;吴建茹;刘小瑜;徐梦丹
广东药科大学药学院,广东 广州 510006广东省深圳市药物警戒和风险管理研究院,广东 深圳 518100广东省深圳市药物警戒和风险管理研究院,广东 深圳 518100广东省深圳市药物警戒和风险管理研究院,广东 深圳 518100广东省深圳市药物警戒和风险管理研究院,广东 深圳 518100广东药科大学药学院,广东 广州 510006||国家药品监督管理局药物警戒技术研究与评价重点实验室,广东 广州 510006
医药卫生
药物警戒社交媒体药品不良反应人工智能可视化分析自然语言处理
pharmacovigilancesocial mediaadverse drug reactionsartificial intelligencevisual analysisnatural language processing
《中国药业》 2026 (7)
9-17,9
国家药品监督管理局药物警戒技术研究与评价重点实验室开放课题[2025ywjjkf03]广东省基础与应用基础研究基金项目[2023A1515011495]广东省药品监督管理局科技创新项目[2025TDB27,2024TDB17]广东省深圳市科技计划项目[JCYJ20230807150659006].
评论