Article
MONITORING AND PREDICTION OF SIDE EFFECTS FROM POLYPHARMACY-INDUCED INTERACTIONS
Detecting side-effects arising from adverse drug–drug interactions (DDIs) has become a crucial focus in modern pharmacovigilance, driven by the widespread use of polypharmacy and the growing need for automated, data-driven safety monitoring. Existing research demonstrates significant progress in DDI prediction, adverse drug reaction (ADR) detection, and pharmacological feature modeling through methods such as label propagation, multi-dimensional feature fusion, graph neural networks (GNNs), and deep neural architectures [1–14]. Recent studies emphasize the increasing reliance on real-world evidence, spontaneous reporting systems, electronic health records, and curated datasets such as TWOSIDES, OFFSIDES, DrugBank, and FAERS to improve the reliability of interaction-based ADR identification [4–7, 15–20]. Building upon these advancements, this work proposes an enhanced DDIbased side-effect detection framework that integrates molecular representation learning, spatiostructural drug-feature fusion, and signal-detection analysis to accurately identify harmful interaction-induced reactions. Leveraging insights from network-based inference, statistical disproportionality methods, and interpretable machine learning models, the system aims to improve prediction accuracy while reducing false positive signals. The study contributes a unified analysis of traditional pharmacovigilance techniques and contemporary AI-driven approaches, highlighting their strengths, limitations, and applicability to real-world clinical settings. The proposed model aligns with emerging trends in intelligent drug-safety surveillance and offers a scalable, explainable solution suitable for large-scale healthcare environments.
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