Unveiling Hidden Risks in Medication Combinations with Graph-Based Adaptive Learning

Abstract

Adverse Drug Reactions (ADRs) caused by drug-drug interactions (DDIs) remain a significant global public health concern, contributing to increased mortality, prolonged morbidity, and escalating healthcare costs. With the growing complexity of modern pharmacological treatments and the expanding elderly population, accurately identifying potential ADRs before they manifest clinically is becoming increasingly critical. Traditional post-market surveillance methods rely heavily on patient-reported side effects, resulting in delayed detection and response. To address this limitation, a novel graph-based adaptive learning approach is proposed for early ADR identification. The “facets DrugBank” dataset serves as the core data source, offering comprehensive information on drug interactions and associated side effects. Instead of conventional techniques like k-Nearest Neighbors (KNN) and Decision Trees, which often fall short in capturing non-linear and complex inter-drug relationships, advanced deep learning techniques are introduced. Drug interactions are modeled as graphs, allowing the application of Graph Neural Networks (GNNs) to learn intricate patterns and relational structures within the data. To further enhance the feature representation, 2D Convolutional Neural Networks (CNNs) are integrated to extract meaningful spatial patterns from the graph-based data structures. The combination of GNNs and CNNs facilitates a robust and scalable architecture, significantly improving prediction capabilities. The proposed model achieves an impressive accuracy of 99.74%, outperforming existing baseline algorithms. This advancement not only enables more precise ADR detection but also offers a proactive solution to reduce adverse outcomes associated with polypharmacy, ultimately supporting safer and more effective medication practices on a population scale. The integration of graph learning with deep feature extraction sets a new standard in pharmacovigilance and highlights the transformative potential of AI-driven healthcare analytics.