Machine learning strategies for predicting pediatric suicidal behaviors in a Brazilian emergency setting

Machine learning, specifically Random Forest models together with oversampling and SHAP, demonstrates strong potential for identifying suicidal risk in pediatric emergency settings. By integrating clinical data with social determinants, these models provide a transparent and scalable strategy for early identification in regions with limited specialized psychiatric resources.