A Recommender System Approach for Predicting Drug Side Effects
Diego Galeano, Alberto Paccanaro · 2018
The accurate identification of drug side effects represents a major concern for public health. We propose a collaborative filtering model for large-scale prediction of drug side effects. Our approach provides side effects recommendations for drugs to safety professionals. The proposed latent factor model relies solely on the public drug-side effect relationships from safety data. Applied to 1,525 marketed drugs and 2,050 side effect terms, we achieved an AUPRC (area under the precision- recall curve) of 0.342 in a test set, with a sensitivity of 0.73 given a specificity of 0.95, providing state-of-the-art performance in side effect prediction. We analyze the performance of the method on drug-specific Anatomical Therapeutic and Chemical (ATC) category and side effect- specific medical category of disorders. Our findings suggest that latent factor models can be useful for the early and accurate detection of unknown adverse drug events.