We read with great interest the article by Luo et al. describing the use of six diagnostic models established using machine learning based
on routine laboratory indicators in differentiating active tuberculosis from latent
tuberculosis infection.
1
Among the six models they built, the optimal performance was obtained from the gradient
boosting machine model (GBM), with a sensitivity of 84.4% and a specificity of 92.7%.
They concluded that the GBM model may be of great benefit as a tool for accurate identification
of active tuberculosis.Keywords
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References
- Machine learning based on routine laboratory indicators promoting the discrimination between active tuberculosis and latent tuberculosis infection.J Infect. 2022; 84: 648-657
- Advances in the microbiology of Stenotrophomonas maltophilia.Clin Microbiol Rev. 2021; 34
- Treatment options for carbapenem-resistant Gram-negative bacterial infections.Clin Infect Dis. 2019; 69: 565-575
- Evaluation of the Vitek 2, Phoenix, and Microscan for antimicrobial susceptibility testing of Stenotrophomonas maltophilia.J Clin Microbiol. 2021; 59e0065421
- Rapid identification of methicillin-resistant Staphylococcus aureus using MALDI-TOF MS and machine learning from over 20,000 clinical isolates.Microbiol Spectr. 2022; 10e0048322
- Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning.Nat Med. 2022; 28: 164-174
- Rapid detection of KPC-producing Klebsiella pneumoniae in China based on MALDI-TOF MS.J Microbiol Methods. 2022; 192106385
- Clinically applicable system for rapidly predicting Enterococcus faecium susceptibility to vancomycin.Microbiol Spectr. 2021; 9e0091321
- Mechanisms of antimicrobial resistance in Stenotrophomonas maltophilia: a review of current knowledge.Expert Rev Anti Infect Ther. 2020; 18: 335-347
- Multidrug efflux pumps: structure, function and regulation.Nat Rev Microbiol. 2018; 16: 523-539
Article info
Publication history
Published online: September 08, 2022
Accepted:
September 5,
2022
Identification
Copyright
© 2022 The British Infection Association. Published by Elsevier Ltd. All rights reserved.
