Interpretable machine learning of clinical MALDI-TOF spectra discriminates carbapenem-resistant <i>Klebsiella pneumoniae</i> while revealing phylogenetic heterogeneity that limits model generalizability.

ABSTRACT:

Introduction

Carbapenem-resistant Klebsiella pneumoniae (CRKP) poses a significant public health threat. Rapid detection of CRKP and its resistance mechanisms is essential for optimizing antibiotic therapy and infection control. However, clinical implementation faces several challenges.

Methods

Machine learning classifiers were applied using MALDI-TOF MS data to discriminate KPC-type, NDM-type CRKP, and carbapenem-susceptible strains (CSKP). Model performance was validated across platforms and strain collections. SHapley Additive exPlanations (SHAP) analysis and phylogenetic reconstruction were used to interpret feature contributions and genetic determinants.

Results

Significant spectral divergence was observed among K. pneumoniae phenotypes, particularl

SUBMITTER: Cai C 

PROVIDER: S-EPMC12648985 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature