目的 探讨世界范围内机器学习在药物警戒领域的应用现状和发展趋势，为开展机器学习在药物警戒领域的应用相关研究提供参考。方法 在Web of Science文献库中以"机器学习""药物警戒"等为主题词检索相关文献，检索时限为建库至2023年3月1日，利用R语言等软件定量分析该领域文献数据，对年度发文量、机构、国家、关键词等方面的特征开展聚类、共现和突现的可视化分析。结果 共纳入904篇文献，文献发表量自1994年以来呈现波动上升趋势。合作网络机构之间存在跨领域、跨地区、跨结构的合作。发文量前5位的国家为美国、中国、日本、韩国、印度，中美在该领域的合作相对较密切。信号检测、社交媒体、电子健康记录是该领域的高频关键词，聚类和关联规则分析显示，机器学习在该领域主要围绕信号识别、非结构化文本挖掘分析和电子医疗信息的处理分析三方面开展研究，目前在信号识别、社交媒体信息挖掘、电子医疗信息非结构化的文本处理等方面取得了显著进展，拓宽了药物警戒的数据来源，提高了对药品不良反应的实时监测能力。结论 大数据和人工智能技术的飞速发展使得机器学习与药物警戒领域融合日益密切，技术交流合作和学科间的交叉融合日益频繁，应先优化各种机器学习算法，提高其在药物警戒领域中的准确性和稳定性;需要加强对数据隐私和安全性的保护措施，确保患者信息的安全;整合医学、数据科学、统计学等领域的专业知识，以期推动药物警戒领域的技术进步。
Bibliometric analysis of the application of machine learning in pharmacovigilance
Objective To explore the application status and development trend of machine learning in the field of pharmacovigilance worldwide,and to provide reference for the research on the application of machine learning in the field of pharmacovigilance.Methods Relevant literature was searched in the Web of Science with the key words of"machine learning"and"pharmacovigilance"from the inception to March 1,2023.R language and other software were used to quantitatively analyze the literature data in this field.The clustering,co-occurrence and emergence visual analysis were carried out on the characteristics of annual published papers,institutions,countries,keywords and other aspects.Results A total of 904 literature were included.The number of literature published showed a fluctuating upward trend since 1994.There was cross-regional,cross-regional and cross-agency cooperation among the cooperative network institutions.The top 5 countries in the number of publications were the United States,China,Japan,South Korea and India,China and the United States had relatively close cooperation in this field.Signal detection,social media and electronic health records were high-frequency keywords in this field.Clustering and association rule analysis showed that this field focused on three aspects signal recognition,unstructured text mining and analysis,and processing and analysis of electronic medical information.At present,machine learning has made significant progress in signal recognition,social media information mining,and unstructured text processing of electronic medical information,which broaden the data sources of pharmacovigilance,improve the real-time monitoring ability of adverse drug reactions,bringing innovation impetus to the field of pharmacovigilance.Conclusion The rapid development of big data and artificial intelligence technologies has led to an increasing integration of machine learning into the field of pharmacovigilance,which promotes technical exchanges and cooperation and cross-disciplinary integration.It is necessary to optimize each machine learning algorithm to improve its accuracy and stability in pharmacovigilance,strengthen the protection measures of data privacy and security to ensure the safety of patient information.Integrating expertise in the fields of science,medicine,and data statistics with a view to promoting technological progress in the field of pharmacovigilance.
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