Adverse events caused by drug-drug interaction (DDI) not only pose a serious threat to health, but also increase additional medical care expenditure. However, despite the emergence of many excellent text mining-based DDI classification methods, achieving a balance between using simpler method and better model performance is still unsatisfactory. In this article, we present a deep learning method of stacked bidirectional Gated Recurrent Unit (GRU)- convolutional neural network (SGRU-CNN) model which apply stacked bidirectional GRU (BiGRU) network and convolutional neural network (CNN) on lexical information and entity position information respectively to conduct DDIs extraction task. Furthermore, SGRU-CNN model assigns the weights of each word feature to improve performance with one attentive pooling layer. On the condition that other values are not inferior to other algorithms, experimental results on the DDI Extraction 2013 corpus show that our model achieves a 1.54% improvement in recall value. And the proposed SGRU-CNN model reaches great performance (F1-score: 0.75) with the fewest features, indicating an excellent balance between avoiding redundant preprocessing task and higher accuracy in relation extraction on biomedical literature using our method.

中文翻译：

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通过生物医学文献上的混合神经网络进行药物相互作用的提取。

药物相互作用引起的不良事件不仅严重威胁健康，还增加了额外的医疗保健支出。但是，尽管出现了许多出色的基于文本挖掘的DDI分类方法，但在使用更简单的方法和更好的模型性能之间取得平衡仍然不能令人满意。在本文中，我们提出了一种堆叠双向门控递归单元（GRU）-卷积神经网络（SGRU-CNN）模型的深度学习方法，该模型将堆叠双向GRU（BiGRU）网络和卷积神经网络（CNN）应用于词汇信息和实体位置信息分别进行DDI提取任务。此外，SGRU-CNN模型分配每个单词特征的权重以改善一个集中池层的性能。在其他值不劣于其他算法的情况下，DDI Extraction 2013语料库的实验结果表明，我们的模型在召回值上提高了1.54％。所提出的SGRU-CNN模型以最少的功能达到了较高的性能（F1-分数：0.75），这表明在避免重复的预处理任务与使用我们的方法对生物医学文献进行关系提取的准确性更高之间取得了很好的平衡。