Explainable artificial intelligence in ICU admission prediction for methanol poisoning patients: a comparative study of deep and machine learning models.

While both ML and DL models demonstrated strong performance, ML models, especially XGBoost, proved to be more effective in predicting ICU admissions for methanol poisoning patients.

The proposed model is intended to support early ICU admission risk stratification during the initial emergency department assessment and should be used as a decision-support tool rather than a replacement for clinical judgment.