Early prediction and risk assessment of adverse drug combinations using ensemble learning

Modern day healthcare has seen an increase in polypharmacy, which is the prescription of multiple drugs as medication to treat illnesses simultaneously. Therefore there is an increased risk of adverse drug reactions resulting from drug-drug interactions. Existing techniques in the field of pharmacovigilance suffer from many drawbacks. Many machine learning approaches using single models face difficulties in identifying complex interaction patterns. Some methods also overlook the fact that a single adverse event may contain a large number of drugs. In terms of interpretability in a clinical context, mere predicting a combination to be risky or not may not provide a clear enough picture. In an effort to address these challenges, in this study, we propose an ensemble learning approach to effectively predict adverse drug combinations using data obtained from the FDA Adverse Event Reporting System (FAERS). Our proposed framework also makes use of DrugBank for mapping drugs and incorporates binary feature vector representations to handle the complexities of the pharmacovigilance data. The ensemble model developed in this study composed of logistic regression, random forest, and CatBoost algorithms proved to be effective compared to several existing techniques in detecting drug interactions with an accuracy of 93.6%, recall of 97.9%, ROC-AUC of 97.5% and PR-AUC of 96%. In addition to achieving strong predictive performance, the model also calculates a confidence score representing the risk associated with specific drug combinations. These results show how ensemble learning can help to enhance the detection of adverse drug reactions and serve as a clinical decision support tool.