Predicting resistance to fluoroquinolones among patients with rifampicin-resistant tuberculosis using machine learning methods is a research paper published in PLOS Digital Health (2022). On theSindex it has a DataRank of 0. It has been cited 6 times.
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United States Agency for International Development
Grant: GHN-A-00-08-00004
National Institute of Allergy and Infectious Diseases
Grant: K01AI119603
National Institute of Allergy and Infectious Diseases
Grant: R01AI112438
National Institute of Allergy and Infectious Diseases
Grant: R01AI146555
Eunice Kennedy Shriver National Institute of Child Health and Human Development
Grant: F30HD105440
National Institute of General Medical Sciences
Grant: T32GM007205
NIAID NIH HHS
Grant: P01 AI159402
National Institutes of Health
Grant: 5R01AI112438-05
Evaluating health and economic effects of targeted strategies in TB/HIV
National Institutes of Health
Grant: 5R01AI146555-03
Optimal targeting for individual and population-level TB prevention
National Institutes of Health
Grant: 5K01AI119603-04
Improving the control of multidrug-resistant tuberculosis through targeted screening and use of novel anti-tuberculosis drugs
National Institutes of Health
Grant: 5T32GM007205-23
MEDICAL SCIENTIST TRAINING PROGRAM
National Institutes of Health
Grant: 5F30HD105440-02
Spatial and Decision Analytic Models for Addressing Challenges in Pediatric Tuberculosis Control and Care
FWCI
0.58
Citation Percentile
0.7%
Citation Trend
Fields of Study
Keywords
Sustainable Development Goals