基于框架语义分析的社交网络药品不良事件抽取

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。

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

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.