Predicting Carbapenem Resistance in Hospitalized Patients Using Machine Learning: A Retrospective Analysis of the MIMIC-III Database

Carbapenem-resistant Gram-negative bacteria (CR-GNB) represent a major health challenge due to limited therapeutic options, increased morbidity, and extended hospital stays (Ham et al., 2021). Early prediction of carbapenem resistance can optimize empirical antibiotic therapy and reduce unnecessary use of broad-spectrum antibiotics. This study developed and validated an artificial intelligence model to predict carbapenem resistance among hospitalized and ICU patients using clinical, demographic, laboratory, and antibiogram data. A retrospective analysis was performed on 94,000 hospitalized patients at Beth Israel Deaconess Medical Centre, utilizing the MIMIC-III (v1.4) database (Johnson et al., 2016). Predictor variables included were demographic characteristics, comorbidities, ICU admission, antibiotic exposure history, inflammatory and biochemical markers, and antibiotic susceptibility test results. Three artificial intelligence algorithms were evaluated: decision trees, random forests, and XGBoost. Model performance was assessed using AUC, precision, sensitivity, specificity, and confusion matrices. The XGBoost model demonstrated the highest performance, achieving an AUC of 0.95, precision of 0.98, sensitivity of 0.90, and specificity of 0.99. These results demonstrate strong discrimination ability and significant potential for integration into clinical workflows. The findings support the use of machine learning to enhance infection prevention, improve antibiotic stewardship, and inform early clinical decision-making.