Abstract
Adverse drug events (ADEs) are an important aspect of drug safety. Various texts such as biomedical literature, drug reviews, and user posts on social media and medical forums contain a wealth of information about ADEs. Recent studies have applied word embedding and deep learning -based natural language processing to automate ADE detection from text. However, they did not explore incorporating explicit medical knowledge about drugs and adverse reactions or the corresponding feature learning. This paper adopts the heterogenous text graph which describes relationships between documents, words and concepts, augments it with medical knowledge from the Unified Medical Language System, and proposes a concept-aware attention mechanism which learns features differently for the different types of nodes in the graph. We further utilize contextualized embeddings from pretrained language models and convolutional graph neural networks for effective feature representation and relational learning. Experiments on four public datasets show that our model achieves performance competitive to the recent advances and the concept-aware attention consistently outperforms other attention mechanisms.
Abstract (translated)
不良反应(ADEs)是药品安全性的一个重要方面。各种文本,如生物医学文献、药品综述和用户在社交媒体和医学论坛中的用户帖子,包含了大量关于ADEs的信息。最近的研究表明,应用词嵌入和深度学习-based自然语言处理从文本中自动检测ADEs的方法非常有前途。但是,他们并没有探索将 explicit medical knowledge 包括药物和不良反应的相关信息或相应的特征学习。本文采用不同的文本图来描述文档、单词和概念之间的关系,并从统一医学语言系统获取医学知识,并提出了一种概念aware注意力机制,该注意力机制为不同类型的节点在图中学习特征而不同。我们进一步使用预训练语言模型和卷积图神经网络的上下文嵌入来有效的特征表示和关系学习。对四个公共数据集的实验表明,我们的模型表现出与最近的进展相当的性能,而概念aware注意力 consistently 优于其他注意力机制。
URL
https://arxiv.org/abs/2301.10451
