2022
Predicting resistance to fluoroquinolones among patients with rifampicin-resistant tuberculosis using machine learning methods
You S, Chitwood MH, Gunasekera KS, Crudu V, Codreanu A, Ciobanu N, Furin J, Cohen T, Warren JL, Yaesoubi R. Predicting resistance to fluoroquinolones among patients with rifampicin-resistant tuberculosis using machine learning methods. PLOS Digital Health 2022, 1: e0000059. PMID: 36177394, PMCID: PMC9518704, DOI: 10.1371/journal.pdig.0000059.Peer-Reviewed Original ResearchDrug susceptibility testXpert MTB/RIFMachine learning-based modelsLearning-based modelsMachine learning methodsRifampicin-resistant tuberculosisTime of diagnosisRifampin-resistant tuberculosisMTB/RIFNeural network modelLearning methodsNetwork modelMulti-drug resistant tuberculosisNational TB surveillanceDrug-resistant tuberculosisOptimism-corrected areaSelection of antibioticsAnti-TB agentsDistrict-level prevalenceLow-resource settingsPatient characteristicsResistant tuberculosisTB surveillanceAppropriate treatmentDST results
2012
Identifying multidrug resistant tuberculosis transmission hotspots using routinely collected data
Manjourides J, Lin HH, Shin S, Jeffery C, Contreras C, Santa Cruz J, Jave O, Yagui M, Asencios L, Pagano M, Cohen T. Identifying multidrug resistant tuberculosis transmission hotspots using routinely collected data. Tuberculosis 2012, 92: 273-279. PMID: 22401962, PMCID: PMC3323731, DOI: 10.1016/j.tube.2012.02.003.Peer-Reviewed Original ResearchConceptsDrug sensitivity testTransmission hotspotsRetreatment casesDrug-resistant tuberculosis epidemicRisk of MDRTime of diagnosisDrug-resistant diseaseTB casesResistant diseaseTuberculosis epidemicHigh riskUntreated casesProgrammatic dataMDRTBRiskMDRHigh levelsTargeted investigationGeographic areasCasesDiseaseDiagnosisSensitivity tests