An explainable machine learning approach to predicting carbapenem resistance in Klebsiella pneumoniae with imbalanced data and resistance-ratio validation

Background

Carbapenem-resistant Klebsiella pneumoniae (CRKP) is a major global health threat. Predicting resistance from electronic medical records (EMRs) may support infection control and antimicrobial stewardship, but class imbalance can impair detection of resistant isolates. This study developed an interpretable machine-learning framework for predicting carbapenem-resistant versus carbapenem-susceptible K. pneumoniae.