Extracting Drug-Drug Interaction from the Biomedical Literature Using a Stacked Generalization-Based Approach

Drug-drug interaction (DDI) detection is particularly important for patient safety. However, the amount of biomedical literature regarding drug interactions is increasing rapidly. Therefore, there is a need to develop an effective approach for the automatic extraction of DDI information from the biomedical literature. In this paper, we present a Stacked Generalization-based approach for automatic DDI extraction. The approach combines the feature-based, graph and tree kernels and, therefore, reduces the risk of missing important features. In addition, it introduces some domain knowledge based features (the keyword, semantic type, and DrugBank features) into the feature-based kernel, which contribute to the performance improvement. More specifically, the approach applies Stacked generalization to automatically learn the weights from the training data and assign them to three individual kernels to achieve a much better performance than each individual kernel. The experimental results show that our approach can achieve a better performance of 69.24% in F-score compared with other systems in the DDI Extraction 2011 challenge task.药物相互作用（Drug-drug Interaction, DDI）检测对于患者安全至关重要。然而，有关药物相互作用的生物医学文献数量正快速增长。因此，亟需开发一种能够从生物医学文献中自动提取DDI信息的有效方法。本文提出了一种基于堆叠泛化（Stacked Generalization）的DDI自动提取方法。该方法融合了基于特征的核函数、图核函数与树核函数，从而降低了遗漏重要特征的风险。此外，该方法还将基于领域知识的特征（包括关键词特征、语义类型特征以及DrugBank特征）融入基于特征的核函数中，有助于提升模型性能。更具体地说，该方法利用堆叠泛化从训练数据中自动学习权重，并将权重分配至三个独立的核函数，从而获得比单个核函数更优异的性能。实验结果表明，相较于2011年DDI提取挑战赛任务中的其他系统，本方法的F1值可达69.24%，性能更优。