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Klebsiella Infections / Boosting Machine Learning Algorithms / Machine Learning · Journal article
Diagnostic Microbiology and Infectious Disease · July 28, 2026
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This is a single-center proof-of-concept study of LightGBM classifiers trained on kernel density-encoded MALDI-TOF spectra to predict K. pneumoniae antimicrobial resistance for 12 antibiotics. Internal hold-out validation yielded accuracy 0.81–0.92 and AUROC 0.88–0.96, but the authors explicitly state that external validation is required before clinical use can be considered.
Machine learning model development with internal hold-out validation. K. pneumoniae isolates (n=424): environmental and human-derived sources combined, stratified into balanced binary datasets per antibiotic (98 ± 8 susceptible, 98 ± 8 resistant per antibiotic). Intermediate phenotypes excluded.. Intervention: LightGBM machine learning models trained on kernel density-encoded MALDI-TOF MS spectra to predict resistance to 12 antibiotics: amikacin, aztreonam, ciprofloxacin, meropenem, piperacillin-tazobactam, cefepime, cefmetazole, cefoperazone-su…. n = 424. Not stated.
Internal hold-out model accuracy ranged from 0.81 to 0.92 across 12 antibiotic-specific models AUROC values ranged from 0.88 to 0.96 for the 12 models Total 424 isolates included with average 98 ± 8 susceptible and 98 ± 8 resistant isolates per antibiotic
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If externally validated, same-day AMR prediction from routine MALDI-TOF spectra could accelerate targeted therapy in K. pneumoniae infections. However, the authors explicitly note that external validation is prerequisite to clinical deployment; clinicians should await independent confirmation before adoption.
Single-center proof-of-concept validation of a machine-learning classifier for antimicrobial resistance prediction using MALDI-TOF spectra, with internal hold-out testing only and no external validation, before clinical application.
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If externally validated, same-day AMR prediction from routine MALDI-TOF spectra could accelerate targeted therapy in K. pneumoniae infections. However, the authors explicitly note that external validation is prerequisite to clinical deployment; clinicians should await independent confirmation before adoption.
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Antimicrobial resistance(AMR) in Klebsiella pneumoniae is an urgent clinical challenge. Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry(MALDI-TOF-MS) is routinely used for species identification, and reusing these spectra for same-day AMR prediction could accelerate targeted therapy. We developed and validated Light Gradient Boosting Machine(LightGBM) models to predict K. pneumoniae resistance directly from routine Bruker MALDI-TOF MS spectra of single bacterial colonies obtained on routine Bruker instruments, using a kernel density-encoded feature representation. Raw spectra were processed to extract peak-level attributes, including m/z, signal-to-noise ratio, peak area and intensity. The m/z axis was intervalized using a kernel density-guided strategy to preserve local spectral density and ordering, and peak attributes within each interval were aggregated into a structured high-dimensional matrix. A total of 424 isolates were included, and balanced binary datasets were constructed for 12 antibiotics, with an average of 98 ± 8 susceptible and 98 ± 8 resistant isolates per antibiotic; intermediate isolates were not included. The dataset included both environmental and human-derived isolates, and antimicrobial susceptibility labels were determined by broth microdilution for amikacin, aztreonam, ciprofloxacin, meropenem, piperacillin-tazobactam, cefepime, cefmetazole, cefoperazone-sulbactam, cefotaxime, ceftazidime, imipenem, and ceftazidime-avibactam, with stronger agreement for cefotaxime and comparatively lower agreement for imipenem. In internal hold-out validation, model accuracy ranged from 0.81 to 0.92 across 12 evaluable antibiotic-specific models, with AUROC values ranging from 0.88 to 0.96. These findings suggest that kernel density-encoded MALDI-TOF MS spectra combined with LightGBM may provide a proof-of-concept framework for AMR prediction, although independent external validation is required before clinical application can be considered.
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