Adverse drug reaction (ADR) detection is an essential task in the medical field, as ADRs have a gravely detrimental impact on patients' health and the healthcare system. Due to a large number of people sharing information on social media platforms, an increasing number of efforts focus on social media data to carry out effective ADR detection. Despite having achieved impressive performance, the existing methods of ADR detection still suffer from three main challenges. Firstly, researchers have consistently ignored the interaction between domain keywords and other words in the sentence. Secondly, social media datasets suffer from the challenges of low annotated data. Thirdly, the issue of sample imbalance is commonly observed in social media datasets. To solve these challenges, we propose the Knowledge Enhanced Shallow and Deep Transformer(KESDT) model for ADR detection. Specifically, to cope with the first issue, we incorporate the domain keywords into the Transformer model through a shallow fusion manner, which enables the model to fully exploit the interactive relationships between domain keywords and other words in the sentence. To overcome the low annotated data, we integrate the synonym sets into the Transformer model through a deep fusion manner, which expands the size of the samples. To mitigate the impact of sample imbalance, we replace the standard cross entropy loss function with the focal loss function for effective model training. We conduct extensive experiments on three public datasets including TwiMed, Twitter, and CADEC. The proposed KESDT outperforms state-of-the-art baselines on F1 values, with relative improvements of 4.87%, 47.83%, and 5.73% respectively, which demonstrates the effectiveness of our proposed KESDT.

翻译：药物不良反应检测是医学领域的重要任务，因为不良反应严重危害患者健康及医疗体系。由于大量用户在社交媒体平台分享信息，越来越多的研究聚焦于社交媒体数据以实现有效的药物不良反应检测。尽管现有方法取得了显著性能，但仍面临三大挑战：首先，研究者长期忽略领域关键词与句子中其他词之间的交互关系；其次，社交媒体数据集存在标注数据不足的问题；第三，社交媒体数据集中普遍存在样本不平衡现象。为解决这些挑战，我们提出知识增强浅层与深层Transformer模型（KESDT）用于药物不良反应检测。具体而言，针对第一个问题，我们通过浅层融合方式将领域关键词融入Transformer模型，使模型充分利用句子中领域关键词与其他词之间的交互关系；针对标注数据不足，通过深层融合方式将同义词集融入Transformer模型以扩展样本规模；为缓解样本不平衡的影响，采用焦点损失函数替代标准交叉熵损失函数进行高效模型训练。我们在TwiMed、Twitter和CADEC三个公开数据集上开展广泛实验。所提出的KESDT在F1值上分别以4.87%、47.83%和5.73%的相对提升优于现有最优基线模型，充分验证了其有效性。