Machine learning prediction of pharmacist intervention benefit in tuberculosis patients using clinical parameters: a single-center retrospective study

Our study demonstrates that comprehensive machine learning optimization can achieve strong predictive performance for identifying patients who would benefit from pharmacist intervention. The clinical prediction model, trained exclusively on clinical variables, provides a robust framework for personalized TB treatment resource allocation. Supportive transcriptomic analyses provide biological context but are not used in model prediction. The model's accuracy (92.25%) and discriminative ability (AUC 96.96%) suggest potential for clinical implementation.