Discover potential adverse drug reactions using the skip-gram model

Abstract

In these years, the adverse drug reactions (ADRs) have seriously impacted the people's health, and adverse drug event reporting systems become a key means to monitor the drug safety, in which healthcare professionals or drug consumers can submit the adverse drug event reports based on their experience or professional knowledge. However, with the increase of drugs, the number of the submitted reports increases rapidly, making it more and more difficult to capture all the ADRs manually. To tackle the problem, we develop a novel system to compute the similarities among the drugs and adverse reactions automatically from the reports. In the method, we represent the mentions of drugs and adverse reactions as distributed vectors using the skip-gram model, and discover the most potential adverse drug reactions based on the similarities.