Feature selection and prediction of treatment failure in tuberculosis
Autor: | David Sasson, Ned McCague, Ben Min-Woo Illigens, Christopher Martin Sauer, Iván Sánchez Fernández, Kenneth E. Paik, Leo Anthony Celi |
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Jazyk: | angličtina |
Rok vydání: | 2018 |
Předmět: |
0301 basic medicine
Bacterial Diseases Male Support Vector Machine Extensively Drug-Resistant Tuberculosis Antitubercular Agents lcsh:Medicine Drug resistance Diagnostic Radiology Machine Learning 0302 clinical medicine Mathematical and Statistical Techniques Risk Factors Medicine and Health Sciences 030212 general & internal medicine Treatment Failure lcsh:Science Microscopy Multidisciplinary Pharmaceutics Radiology and Imaging Statistics Middle Aged Pulmonary Imaging Infectious Diseases Physical Sciences Female Research Article Adult medicine.medical_specialty Computer and Information Sciences Tuberculosis Imaging Techniques MEDLINE Developing country Research and Analysis Methods Microbiology 03 medical and health sciences Pharmacotherapy Drug Therapy Artificial Intelligence Diagnostic Medicine Internal medicine Support Vector Machines Microbial Control medicine Humans Statistical Methods Pharmacology business.industry lcsh:R Extensively drug-resistant tuberculosis Biology and Life Sciences Stepwise regression medicine.disease Missing data Tropical Diseases 030104 developmental biology lcsh:Q Antimicrobial Resistance business Mathematics Forecasting |
Zdroj: | PLoS ONE, Vol 13, Iss 11, p e0207491 (2018) PLoS ONE |
ISSN: | 1932-6203 |
Popis: | Background Tuberculosis is a major cause of morbidity and mortality in the developing world. Drug resistance, which is predicted to rise in many countries worldwide, threatens tuberculosis treatment and control. Objective To identify features associated with treatment failure and to predict which patients are at highest risk of treatment failure. Methods On a multi-country dataset managed by the National Institute of Allergy and Infectious Diseases we applied various machine learning techniques to identify factors statistically associated with treatment failure and to predict treatment failure based on baseline demographic and clinical characteristics alone. Results The complete-case analysis database consisted of 587 patients (68% males) with a median (p25-p75) age of 40 (30–51) years. Treatment failure occurred in approximately one fourth of the patients. The features most associated with treatment failure were patterns of drug sensitivity, imaging findings, findings in the microscopy Ziehl-Nielsen stain, education status, and employment status. The most predictive model was forward stepwise selection (AUC: 0.74), although most models performed at or above AUC 0.7. A sensitivity analysis using the 643 original patients filling the missing values with multiple imputation showed similar predictive features and generally increased predictive performance. Conclusion Machine learning can help to identify patients at higher risk of treatment failure. Closer monitoring of these patients may decrease treatment failure rates and prevent emergence of antibiotic resistance. The use of inexpensive basic demographic and clinical features makes this approach attractive in low and middle-income countries. |
Databáze: | OpenAIRE |
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