An Improved Apriori Algorithm for Detecting Drug-Drug Interactions in VAERS Database

Music City Center 

The Vaccine Adverse Event Reporting System (VAERS), jointly overseen by the Centers for Disease Control and Prevention (CDC) and the US Food and Drug Administration (FDA), is designed to identify potential safety issues associated with vaccines licensed in the United States. However, data mining within VAERS is challenging due to the dataset's high dimensionality and the complex confounding among adverse events and vaccines. Moreover, drug-drug interactions (DDIs) can modify the effects of individual drugs, leading to adverse events that are rarely observed when the drugs are administered alone. Given the difficulty of assessing DDIs during the pre-marketing stage—when clinical trials typically evaluate single drugs—post-marketing surveillance, particularly through spontaneous reporting systems like VAERS, is crucial for detecting previously unknown adverse events attributable to both individual drugs and their interactions. To address this challenge, this talk introduces an improved Apriori algorithm for detecting DDIs in spontaneous reporting systems. We demonstrate its potential through various simulation studies and further validate its performance using the VAERS dataset.

Drug-Drug Interaction

Apriori Algorithm

VAERS