This article discusses the potential use of biomarker models for predicting acute kidney injury (AKI) in sepsis patients. The study used machine learning techniques to analyze biomarker levels and identify a panel of biomarkers that could accurately predict AKI. Results showed that the SVM and Naïve Bayes models had high sensitivity and specificity in predicting AKI. This research has the potential to improve early detection and prevention of AKI in sepsis patients.
