Detecting high-quality signals of adverse drug-drug interactions from spontaneous reporting data

As a medicine safety issue, Drug-Drug Interaction (DDI) may become an unexpected threat for causing Adverse Drug Events (ADEs). There is a growing demand for computational methods to efficiently and effectively analyse large-scale data to detect signals of Adverse Drug-drug Interactions (ADDIs). In this paper, we aim to detect high-quality signals of ADDIs which are non-spurious and non-redundant. We propose a new method which employs the framework of Bayesian network to infer the direct associations between the target ADE and medicines, and uses domain knowledge to facilitate the learning of Bayesian network structures. To improve efficiency and avoid redundancy, we design a level-wise algorithm with pruning strategy to search for high-quality ADDI signals. We have applied the proposed method to the United States Food and Drug Administration's (FDA) Adverse Event Reporting System (FAERS) data. The result shows that 54.45% of detected signals are verified as known DDIs and 10.89% were evaluated as high-quality ADDI signals, demonstrating that the proposed method could be a promising tool for ADDI signal detection.