An Ensemble Machine Learning Model for Early Prediction of Vancomycin-Induced Acute Kidney Injury in ICU Patients.

INTRODUCTION

Acute Kidney Injury (AKI) is a severe complication of vancomycin treatment due to its nephrotoxic effects. However, research on predicting AKI in this high-risk group remains limited. This study presents a stacking ensemble machine learning model designed to predict the onset of AKI in this patient population.

METHODS

Leveraging data from 314 ICU patients, the model incorporates SHapley Additive exPlanations (SHAP) for enhanced interpretability, identifying key predictors such as serum creatinine levels, glucose variability, and patient age. The model achieved an Area Under the Curve (AUC) of 0.94, outperforming existing predictive approaches. By utilizing readily available clinical data and determining an optimal temporal prediction window, this model facilitates proactive clinical decision-making, aiming to reduce the risk of AKI and improve patient outcomes.

RESULTS

The stacking ensemble model achieved 92% accuracy, 93% precision, 92% sensitivity, and 0.94 AUC in 314 ICU patients, pinpointing creatinine, glucose variability, and age as critical AKI predictors.

CONCLUSION

The findings suggest that integrating advanced machine learning techniques with interpretable artificial intelligence (AI) can provide a scalable and cost-effective solution for early AKI detection in diverse healthcare settings.