基于框架语义分析的社交网络药品不良事件抽取  

Extraction of Adverse Drug Events from Social Media Based on FrameNet Semantic Analysis

作　　者：由丽萍[1] 王世钰 李朝翻 YOU Liping;WANG Shiyu;LI Chaofan(College of Economics and Management,Shanxi University,Taiyuan 030006,China)

机构地区：[1]山西大学经济与管理学院,太原030006

出　　处：《医学信息学杂志》2023年第7期57-62,共6页Journal of Medical Informatics

基　　金：国家自然科学基金面上项目(项目编号:61772324)。

摘　　要：目的/意义基于社交网络评论文本抽取药品不良事件,为药品研发和安全监管提供参考。方法/过程采用框架语义理论,结合《监管活动医学词典》术语集构建药品不良事件分类词表;基于词典和规则匹配的方法识别事件类别和框架元素,利用语义信息实现药品不良事件框架填充。结果/结论选取社交网络药品评价实例进行药品不良事件信息抽取可行有效,有助于框架语义分析方法在医疗专业领域的深度应用和价值实现。Purpose/Significance To extract adverse drug events based on the text of social network comments,and to provide references for drug research and safety regulation.Method/Process The FrameNet semantic theory is adopted and combined with the Medical Dictionary of Regulatory Activities terminology set to construct the adverse drug event classification lexicon.The lexicon and rule matching-based approach is used to identify event categories and frame elements,and semantic information is used to achieve the filling of adverse drug event frame.Result/Conclusion The selection of social network drug evaluation examples for adverse drug event information extraction is feasible and effective,which contributes to the in-depth application and value realization of the FrameNet semantic analysis method in the medical field.

关 键 词：社交网络 框架语义 药品不良事件 信息抽取 自然语言处理 

分 类 号：R-058[医药卫生]