摘要
药品不良反应(adverse drug reaction,ADR)报告作为药品上市后安全评价的主要载体,对药物安全评价研究具有重要的参考价值。本文以深度学习模型中的双向门控循环单元(bidirectional gated recurrent unit,Bi-GRU)结构为基础,引入注意力机制以及字向量与分词向量优化模型,对我国ADR报告中的ADR过程描述部分进行"药品-不良反应"的关系抽取研究。实验结果表明,基于深度学习的实体关系抽取模型在确认不良反应描述中"药品-不良反应"对之间的关系(否认、可能、直接和后处理)的分类任务中达到了很好的性能,最终模型取得87.52%的F值。所提取的信息在辅助ADR报告评价的同时,可进一步运用于特定药物的不良反应统计学研究以及知识库构建等任务中,从而为药物安全性评价研究提供更多的研究手段。
Adverse drug reaction(ADR) reports are acting as primary sources for post-marketing drug safety evaluation,which have important reference value for drug safety evaluation.In this article,bidirectional gated recurrent unit,a kind of deep learning method,was applied as the model for relation extraction of drugs and adverse reactions in free-text section of ADR descriptions in Chinese ADR reports,with attention as well as character embedding and word segmentation embedding added into the network.The experimental results showed that our model achieved good performance in the classification task of confirming the relationship of "Drug-ADR" pair(denial,likely,direct and post-therapy) in the ADR description,and the final model achieved an F-value of 87.52%.The extracted information can assist in evaluating ADR reports and at the same time be utilized in tasks like statistical analysis of certain drugs and adverse events and ADR knowledge base construction to provide more research techniques for drug safety researches.
作者
陈瑶
吴红
葛卫红
张海霞
廖俊
CHEN Yao;WU Hong;GE Weihong;ZHANG Haixia;LIAO Jun(School of Science,China Pharmaceutical University,Nanjing 211198;Department of Pharmacy,Nanjing Drum Tower Hospital,Nanjing 210008; Key Laboratory of Drug Quality Control and Pharmacovigilance(China Pharmaceutical University),Ministry of Education,Nanjing 210009,China)
出处
《中国药科大学学报》
CAS
CSCD
北大核心
2019年第6期753-759,共7页
Journal of China Pharmaceutical University
基金
国家自然科学基金资助项目(No.81773806)
双一流创新团队资助项目(No.CPU2018GY19)
江苏省食品药品监督管理局2017—2018年度科研项目资助项目(No.20170308).
关键词
药品不良反应
关系抽取
药物安全评价
深度学习
双向门控循环单元
adverse drug reaction
relation extraction
drug safety evaluation
deep learning
bidirectional gated recurrent unit
作者简介
通信作者:廖俊,Tel:025-86185122,E-mail:liaojun@cpu.edu.cn
