  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.
