This project develops interpretable machine-learning models that combine genome, haplotype, and clinical features to estimate individual risk of adverse drug reactions before a prescription is written. Emphasis is placed on calibration across underrepresented populations and on surfacing the genetic features driving each prediction, so that model outputs can support rather than obscure clinical decision-making.