Comparison of machine learning and logistic regression in predicting mortality from acute poisoning in young adults: a multicenter study identifying herbicide exposure as the predominant risk determinant.

Logistic regression performs similarly to complex machine learning algorithms in predicting the risk of death from acute poisoning in young adults, with better interpretability and clinical practicality.

The nomogram constructed based on this is a simple and effective early risk stratification tool, which can serve as one of the reference tools for clinical decision-making assistance.