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

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

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

Afiliação

You S; Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut, United States of America.
Chitwood MH; Public Health Modeling Unit, Yale School of Public Health, New Haven, Connecticut, United States of America.
Gunasekera KS; Public Health Modeling Unit, Yale School of Public Health, New Haven, Connecticut, United States of America.
Crudu V; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America.
Codreanu A; Public Health Modeling Unit, Yale School of Public Health, New Haven, Connecticut, United States of America.
Ciobanu N; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America.
Furin J; Phthisiopneumology Institute, Chisinau, Republic of Moldova.
Cohen T; Phthisiopneumology Institute, Chisinau, Republic of Moldova.
Warren JL; Phthisiopneumology Institute, Chisinau, Republic of Moldova.
Yaesoubi R; Department of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America.

PLOS Digit Health ; 1(6)2022.

Article em En | MEDLINE | ID: mdl-36177394

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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: PLOS Digit Health Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos

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