Machine Learning Model for Predicting Severe Adverse Events in Oncology Patients Using the US Food and Drug Administration Adverse Event Reporting System

Our gradient boosting model substantially improved prediction and interpretability of severe oncology adverse events compared with logistic regression. SHAP analysis identified clinically meaningful predictors, enabling precision pharmacovigilance and targeted risk mitigation. These findings support ML integration into regulatory and clinical pharmacovigilance workflows to enhance postmarket safety surveillance.