GA-ADE: a novel approach based on graph algorithm to improves the detection of adverse drug events

Abstract

The adverse drug event (ADE) is an unexpected and harmful consequence of drug usege. Identifying the association between the use of drugs and adverse events from biomedical literature can contribute a lot to drug safety supervision. Such identification can not only assist drug safety monitoring, but also correct known dependencies among events. In this paper,we propose a novel approach based on graph algorithm to detect adverse drug events(GA-ADE). In our approach, we first construct a graph using candidate ADE extracted from biomedical literature. We then propose a method to select important vertices from the graph as core vertices, and design a Personal Rank algorithm using the core vertices for clustering to build subgraphs. Lastly, the correlation between the drug and the event is calculated based on the subgraphs. Experiments show that our approach is feasible.