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BACKGROUND Hypervirulent Klebsiella pneumoniae (hvKP) infections can have high morbidity and mortality rates owing to their invasiveness and virulence. However, there are no effective tools or biomarkers to discriminate between hvKP and nonhypervirulent K. pneumoniae (nhvKP) strains. We aimed to use a random forest algorithm to predict hvKP based on core-genome data. METHODS In total, 272 K. pneumoniae strains were collected from 20 tertiary hospitals in China and divided into hvKP and nhvKP groups according to clinical criteria. Clinica
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