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Adverse drug events (ADE) are prevalent and costly. Clinical trials areconstrained in their ability to identify potential ADEs, motivating thedevelopment of spontaneous reporting systems for post-market surveillance.Statistical methods provide a convenient way to detect signals from thesereports but have limitations in leveraging relationships between drugs and ADEsgiven their discrete count-based nature. A previously proposed method, aer2vec,generates distributed vector representations of ADE report entities thatcapture patterns of similarity but cannot utilize lexical knowledge. We addressthis limitation by retrofitting aer2vec drug embeddings to knowledge fromRxNorm and developing a novel retrofitting variant using vector rescaling topreserve magnitude. When evaluated in the context of a pharmacovigilance signaldetection task, aer2vec with retrofitting consistently outperformsdisproportionality metrics when trained on minimally preprocessed data.Retrofitting with rescaling results in further improvements in the larger andmore challenging of two pharmacovigilance reference sets used for evaluation.

Retrofitting Vector Representations of Adverse Event Reporting Data to Structured Knowledge to Improve Pharmacovigilance Signal Detection