Adverse drug event detection and extraction from open data: A deep learning approach
作者：
Highlights：
• Previous pharmacovigilance research fails to accurately discover drug side effects.
• We introduce a novel adverse drug event extraction algorithm using deep learning.
• We introduce the use of novel contextual word and sentence embeddings.
• Results show that our model outperforms current pharmacovigilance models.
• This model can be applied to a wide variety of information extraction tasks.
摘要
•Previous pharmacovigilance research fails to accurately discover drug side effects.•We introduce a novel adverse drug event extraction algorithm using deep learning.•We introduce the use of novel contextual word and sentence embeddings.•Results show that our model outperforms current pharmacovigilance models.•This model can be applied to a wide variety of information extraction tasks.
论文关键词：Information extraction,Deep learning,Pharmacovigilance,Drug side effects,Open data,BERT,Natural language processing
论文评审过程：Received 12 August 2019, Revised 19 September 2019, Accepted 20 September 2019, Available online 22 October 2019, Version of Record 22 October 2019.
论文官网地址：https://doi.org/10.1016/j.ipm.2019.102131
