Predicting resistance to fluoroquinolones among patients with rifampicin-resistant tuberculosis using machine learning methods

Predicting resistance to fluoroquinolones among patients with rifampicin-resistant tuberculosis using machine learning methods

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<h4>Background</h4> Limited access to drug-susceptibility tests (DSTs) and delays in receiving DST results are challenges for timely and appropriate treatment of multi-drug resistant tuberculosis (TB) in many low-resource settings. We investigated whether data collected as part of routin...

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Bibliographic Details
Main Authors: Shiying You, Melanie H. Chitwood, Kenneth S. Gunasekera, Valeriu Crudu, Alexandru Codreanu, Nelly Ciobanu, Jennifer Furin, Ted Cohen, Joshua L. Warren, Reza Yaesoubi
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2022-01-01
Series:PLOS Digital Health
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9518704/?tool=EBI

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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9518704/?tool=EBI

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