Research on entity relation extraction of Chinese adverse drug reaction reports based on deep learning method

CHEN Yao; WU Hong; GE Weihong; ZHANG Haixia; LIAO Jun

doi:10.11665/j.issn.1000-5048.20190617

Journal of China Pharmaceutical University > 2019 > 50(6): 753-759. > DOI: 10.11665/j.issn.1000-5048.20190617

CHEN Yao, WU Hong, GE Weihong, ZHANG Haixia, LIAO Jun. Research on entity relation extraction of Chinese adverse drug reaction reports based on deep learning method[J]. Journal of China Pharmaceutical University, 2019, 50(6): 753-759. DOI: 10.11665/j.issn.1000-5048.20190617

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Research on entity relation extraction of Chinese adverse drug reaction reports based on deep learning method

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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.
- adverse drug reaction,
- relation extraction,
- drug safety evaluation,
- deep learning,
- bidirectional gated recurrent unit

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