An AI/ML-driven patient stratification model was built for an immunotherapy drug developer to classify responders versus non-responders, integrating EHR, genomic, and clinical trial data. The resulting XGBoost model achieved 89% accuracy, 85% sensitivity, and 90% specificity in predicting treatment response, enabling more precise patient selection, reduced side-effect risk, and streamlined clinical trial design.
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