Machine learning prediction of pediatric adverse drug reactions using consensus-derived scarce data

Adverse drug reactions (ADRs) represent a significant cause of morbidity and mortality in children, who face distinct pharmacological vulnerabilities due to unique physiological development. Current pediatric drug safety research is hindered by limited clinical data and adult-focused studies, creating evidence gaps. We developed a comprehensive computational approach for pediatric pharmacovigilance, integrating consensus-driven signal detection, multi-level biological features, and interpretable machine learning. Using 1.4 million FDA Adverse Event Reporting System reports, we constructed the largest curated pediatric drug-ADR dataset. Severity-specific thresholds and voting across four algorithms (PRR, ROR, BCPNN, and EBGM) optimized ADR identification. Multi-level biological fingerprints spanning molecular, target, and network domains combined with XGBoost significantly improved predictive performance (ROC AUC: 0.7177), especially for imbalanced scenarios. Cross-domain analyses revealed that models trained on adult data exhibit poor generalization to pediatric contexts, confirming that adverse reactions in children cannot be reliably predicted using adult data. Our approach successfully identified established and novel pediatric-specific ADRs with strong literature support. Collectively, this work establishes methodological innovations for pediatric pharmacovigilance, bridges a critical evidence gap in pediatric drug safety, and delivers practical tools for clinical and regulatory decision-making.