WATCH VIDEO
Drug side-effects are unfortunately common and are often undetected until after a drug has been released. The current state of the art relies on observing a disproportionate co-occurrence of a drug with a potential side-effect for detection. This methodology does not consider relational or causal information, or similarity between drugs and side-effects for classification. In this work, embeddings are learned from literature-derived relational connections, and are entangled together for pairs of interest and leveraged with supervised machine learning. This composed, relation-based learning (CoRL) produces state of the art performance on two widely used, manually curated reference standards for drug safety monitoring.
