Development and validation of a machine learning stratified prediction model for early warning of anti-tuberculosis drug-induced liver injury risk based on real-world data: a retrospective cohort study

Stratified machine learning models, particularly ensemble methods, demonstrated excellent performance in predicting ATB-DILI risk and highlighted distinct injury mechanisms between general and CHB co-infected TB patients. This approach offers a clinically interpretable and accurate tool for early warning of ATB-DILI, supporting personalized risk assessment and management.