Machine learning prediction of pediatric adverse drug reactions using consensus-derived scarce data
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A computational pharmacovigilance approach for children that integrates consensus-driven signal detection, multi-level biological features, and interpretable machine learning, built on the largest curated pediatric drug–ADR dataset derived from 1.4 million FAERS reports…