论文标题
不利事件事件的矢量表示，将数据报告到结构化知识以改善药物守护信号检测
Retrofitting Vector Representations of Adverse Event Reporting Data to Structured Knowledge to Improve Pharmacovigilance Signal Detection
论文作者
论文摘要
不良药物事件（ADE）普遍存在昂贵。临床试验的识别能力受到限制，激发了用于市场后监视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方便方法，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，该实体捕获相似性但无法利用词汇知识。我们通过将AER2VEC药物嵌入到RXNorm的知识中，并使用矢量恢复以保留幅度来解决新颖的改造变体，来解决这一限制。当在药物宣传信号检测任务的背景下进行评估时，在对最小预处理数据进行训练时，AER2VEC始终超过了不成比例的指标。进行重新改造会导致进一步改善用于评估的两种药物守护参考集的更大和更具挑战性。
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.
