School of Computer Science and Engineering, Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, Nanjing 210096, China.
School of Engineering and Applied Science, Aston University, UK.
Artif Intell Med. 2018 May;87:1-8. doi: 10.1016/j.artmed.2018.03.001. Epub 2018 Mar 17.
A drug-drug interaction (DDI) is a situation in which a drug affects the activity of another drug synergistically or antagonistically when being administered together. The information of DDIs is crucial for healthcare professionals to prevent adverse drug events. Although some known DDIs can be found in purposely-built databases such as DrugBank, most information is still buried in scientific publications. Therefore, automatically extracting DDIs from biomedical texts is sorely needed.
In this paper, we propose a novel position-aware deep multi-task learning approach for extracting DDIs from biomedical texts. In particular, sentences are represented as a sequence of word embeddings and position embeddings. An attention-based bidirectional long short-term memory (BiLSTM) network is used to encode each sentence. The relative position information of words with the target drugs in text is combined with the hidden states of BiLSTM to generate the position-aware attention weights. Moreover, the tasks of predicting whether or not two drugs interact with each other and further distinguishing the types of interactions are learned jointly in multi-task learning framework.
The proposed approach has been evaluated on the DDIExtraction challenge 2013 corpus and the results show that with the position-aware attention only, our proposed approach outperforms the state-of-the-art method by 0.99% for binary DDI classification, and with both position-aware attention and multi-task learning, our approach achieves a micro F-score of 72.99% on interaction type identification, outperforming the state-of-the-art approach by 1.51%, which demonstrates the effectiveness of the proposed approach.
药物-药物相互作用（DDI）是指当两种药物同时给药时，一种药物协同或拮抗地影响另一种药物的活性的情况。DDI 信息对于医疗保健专业人员预防不良药物事件至关重要。尽管一些已知的 DDI 可以在 DrugBank 等专门构建的数据库中找到，但大多数信息仍隐藏在科学出版物中。因此，迫切需要从生物医学文本中自动提取 DDI。
在本文中，我们提出了一种新颖的基于位置感知的深度多任务学习方法，用于从生物医学文本中提取 DDI。特别是，句子表示为词嵌入和位置嵌入的序列。基于注意力的双向长短期记忆（BiLSTM）网络用于对每个句子进行编码。结合文本中带有目标药物的单词的相对位置信息与 BiLSTM 的隐藏状态，生成位置感知注意力权重。此外，在多任务学习框架中共同学习预测两种药物是否相互作用以及进一步区分相互作用类型的任务。
所提出的方法在 DDIExtraction 挑战 2013 语料库上进行了评估，结果表明，仅使用位置感知注意力，我们的方法在二分类 DDI 分类方面比最先进的方法高出 0.99%，而在使用位置感知注意力和多任务学习方面，我们的方法在交互类型识别方面的微 F 分数达到 72.99%，比最先进的方法高出 1.51%，这证明了所提出的方法的有效性。
