Association Discovery Approach in Healthcare Big Data to Identify Drug Safety and Drug Repurposing Signals.

Data science approaches have been increasingly implemented in healthcare big data to evaluate the safety and effectiveness of drugs. Association discovery is a data mining approach that finds potentially associated elements in high-dimensional data. We present a novel implementation of the association discovery approach in longitudinal healthcare data to identify drug safety and drug repurposing signals from positive and inverse associations between drug use and clinical outcomes, respectively. We used the 10% sample data from the Australian Pharmaceutical Benefits Scheme (2014-2024), which comprises prescription claims records. Using the Magnum Opus association discovery tool, we identified associations between a wide range of drugs and three common chronic medical conditions (i.e., coronary artery disease, type 2 diabetes, epilepsy). Cases with the conditions were identified using the supply of indicator drug(s) as a proxy for the conditions and matched to controls not supplied the indicator drug(s). Drug use was defined using Anatomical Therapeutic Chemical (ATC) codes supplied during a one-year lookback period before the supply of the indicator drug(s). We also evaluated combinations of up to four drugs and identified associations at the ATC level of drug class(es). In this study, we reproduced several known adverse drug events and protective drug effects, while some associations were attributable to confounding, mutual indications, or reverse causation. The remaining associations may represent previously uncharacterized drug safety and drug repurposing signals, necessitating further validation. We also discussed methodological differences between this association discovery approach and other similar data science approaches.