Mining Disproportional Frequent Arrangements of Event Intervals for Investigating Adverse Drug Events

Adverse drug events are pervasive and costly medical conditions, in which novel research approaches are needed to investigate the nature of such events further and ultimately achieve early detection and prevention. In this paper, we seek to characterize patients who experience an adverse drug event, represented as a case group, by contrasting them to similar control group patients who do not experience such an event. To achieve this goal, we utilize an extensive electronic patient record database and apply a combination of frequent arrangement mining and disproportionality analysis. Our results have identified how several adverse drug events are characterized in regards to frequent disproportional arrangements, where we highlight how such arrangements can provide additional temporal-based information compared to similar approaches.