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

Show other versions (1)

<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...

Full description

Bibliographic Details
Published in:	PLOS Digital Health
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
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9518704/?tool=EBI

Predicting resistance to fluoroquinolones among patients with rifampicin-resistant tuberculosis using machine learning methods. by Shiying You, Melanie H Chitwood, Kenneth S Gunasekera, Valeriu Crudu, Alexandru Codreanu, Nelly Ciobanu, Jennifer Furin, Ted Cohen, Joshua L Warren, Reza Yaesoubi

Published in PLOS Digital Health (2022-01-01)

Get full text

Article

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. by Shiying You, Melanie H Chitwood, Kenneth S Gunasekera, Valeriu Crudu, Alexandru Codreanu, Nelly Ciobanu, Jennifer Furin, Ted Cohen, Joshua L Warren, Reza Yaesoubi

Similar Items