Predicting treatment outcome of drug-susceptible tuberculosis patients using machine-learning models

Informatics for Health and Social Care

Volumn 44, Issue 2, 2019, Pages 135-151

  Hussain, Owais A ^a   Junejo, Khurum N ^a  

^a KARACHI INSTITUTE OF ECONOMICS AND TECHNOLOGY   (Pakistan)

Author keywords

drug susceptible tuberculosis; ehealth; optimization of health workers; Predicting tuberculosis outcome; predictive analysis; tuberculosis treatment

Indexed keywords

TUBERCULOSTATIC AGENT;

DECISION TREE; DIRECTLY OBSERVED THERAPY; ECONOMICS; HUMAN; INTERATRIAL BLOCK; MACHINE LEARNING; MEDICATION COMPLIANCE; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; TREATMENT OUTCOME; TUBERCULOSIS;

ANTITUBERCULAR AGENTS; DECISION TREES; DIRECTLY OBSERVED THERAPY; HUMANS; INTERATRIAL BLOCK; MACHINE LEARNING; MEDICATION ADHERENCE; MODELS, STATISTICAL; SENSITIVITY AND SPECIFICITY; TREATMENT OUTCOME; TUBERCULOSIS;

EID: 85042218244     PISSN: 17538157     EISSN: 17538165     Source Type: Journal    
DOI: 10.1080/17538157.2018.1433676     Document Type: Article

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