Evaluation of Ensemble Machine Learning Model Utilizing Biomarkers to Predict Multidrug-Resistant Enterobacteriaceae Urosepsis: A Narrative Review

Abstract

Urosepsis primarily caused by multidrug-resistant Enterobacteriaceae is a major public health problem due to high mortality rates and a large diagnostic delay using conventional culture methods. This delay often translates to inappropriate empirical antibiotic treatment, exacerbating the problem of antimicrobial resistance. This review determines if ensemble-based machine learning models, especially those that use inflammatory markers, can help bridge this diagnostic gap. Ensemble based tree models, such as Extreme Gradient Boosting, might outperform traditional models when used to analyze structured clinical data to predict risk more effectively. Using model interpretability, such as SHapley Additive exPlanations, is a key factor to determine that C-reactive protein, procalcitonin, and the ratio of procalcitonin to albumin are key predictors. Implementing these transparent, data-driven systems may facilitate implementation of precision antimicrobial stewardship. By closing this diagnostic window ultimately enables individualized therapeutic interventions, minimizing the inappropriate second-line antibiotic use and improving patient survival rates.