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.
1Among 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.
- Luo Y.
- Xue Y.
- Song H.
- Tang G.
- Liu W.
- Bai H.
- et al.
Machine learning based on routine laboratory indicators promoting the discrimination between active tuberculosis and latent tuberculosis infection.
J Infect. 2022; 84: 648-657
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- Machine learning based on routine laboratory indicators promoting the discrimination between active tuberculosis and latent tuberculosis infection.J Infect. 2022; 84: 648-657
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Published online: September 08, 2022
Accepted: September 5, 2022
© 2022 The British Infection Association. Published by Elsevier Ltd. All rights reserved.