Machine learning triumphs in differentiating fungal and bacterial infections

This research developed machine learning models to differentiate invasive fungal disease from bacterial infections in multiple myeloma patients. By analyzing clinical and laboratory data from 298 cases, researchers found that logistic regression performed best with a 0.967 AUC. Enhanced by SHAP analysis, this model provides clinicians with a practical tool for early identification and treatment.