Ding Xiruo, Cohen Trevor
University of Washington, Seattle, WA, USA.
AMIA Annu Symp Proc. 2021 Jan 25;2020:383-392. eCollection 2020.
Adverse drug events (ADE) are prevalent and costly. Clinical trials are constrained in their ability to identify potential ADEs, motivating the development of spontaneous reporting systems for post-market surveillance. Statistical methods provide a convenient way to detect signals from these reports but have limitations in leveraging relationships between drugs and ADEs given their discrete count-based nature. A previously proposed method, aer2vec, generates distributed vector representations of ADE report entities that capture patterns of similarity but cannot utilize lexical knowledge. We address this limitation by retrofitting aer2vec drug embeddings to knowledge from RxNorm and developing a novel retrofitting variant using vector rescaling to preserve magnitude. When evaluated in the context of a pharmacovigilance signal detection task, aer2vec with retrofitting consistently outperforms disproportionality metrics when trained on minimally preprocessed data. Retrofitting with rescaling results in further improvements in the larger and more challenging of two pharmacovigilance reference sets used for evaluation.
药物不良事件(ADE)普遍存在且代价高昂。临床试验在识别潜在ADE方面能力有限,这推动了用于上市后监测的自发报告系统的发展。统计方法提供了一种从这些报告中检测信号的便捷方式,但鉴于其基于离散计数的性质,在利用药物与ADE之间的关系方面存在局限性。先前提出的方法aer2vec生成了ADE报告实体的分布式向量表示,可捕捉相似性模式,但无法利用词汇知识。我们通过将aer2vec药物嵌入与RxNorm中的知识进行适配,并开发一种使用向量重缩放来保留幅度的新型适配变体,来解决这一局限性。在药物警戒信号检测任务的背景下进行评估时,经过适配的aer2vec在对最少预处理数据进行训练时,始终优于不成比例性指标。在用于评估的两个药物警戒参考集中,规模更大且更具挑战性的参考集中,采用重缩放的适配会带来进一步的改进。