Research on entity relation extraction of Chinese adverse drug reaction reports based on deep learning method
10.11665/j.issn.1000-5048.20190617
- VernacularTitle:基于深度学习模型的我国药品不良反应报告实体关系抽取研究
- Author:
Yao CHEN
1
;
Hong WU
;
Weihong GE
;
Haixia ZHANG
;
Jun LIAO
Author Information
1. 中国药科大学理学院
- Publication Type:Journal Article
- Keywords:
adverse drug reactio;
relation extraction;
drug safety evaluation;
deep learning;
bidirectional gated recurrent unit
- From:
Journal of China Pharmaceutical University
2019;50(6):753-759
- CountryChina
- Language:Chinese
-
Abstract:
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.
