Predicting the need for medical care after toxin exposure using SHAP-interpretable gradient boosting
This study demonstrates that XGBoost models, interpreted via SHAP, can accurately and reliably predict the need for medical care following toxin exposure using only initial poison control center call data, offering a promising tool to support expert triage decisions.