ArticlePloS one2018
Feature selection and prediction of treatment failure in tuberculosis.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Systematic review and meta-analysis of predictive accuracy of prognostic models for poor treatment outcome of drug resistance tuberculosis.Scientific reports · 2026Pooled it
- Impact of alcohol consumption, substance use, and smoking on treatment outcomes in tuberculosis: a systematic review and meta-analysis.Systematic reviews · 2025Pooled it
- Systematic review of prediction models for pulmonary tuberculosis treatment outcomes in adults.BMJ open · 2021Pooled it
- Harnessing the gut microbiome to combat tuberculosis: a technological and clinical review.Frontiers in cellular and infection microbiology · 2026Review
- Integrated Artificial Intelligence Framework for Tuberculosis Treatment Abandonment Prediction: A Multi-Paradigm Approach.Journal of clinical medicine · 2025Article
- Dynamic Prediction of Treatment Failure in Ocular Tuberculosis Using Machine Learning and Explainable AI.Translational vision science & technology · 2025Article
- Using Machine Learning Methods to Predict Early Treatment Outcomes for Multidrug-Resistant or Rifampicin-Resistant Tuberculosis to Enhance Patient Cure Rates: Development and Validation of Multiple Models.Journal of medical Internet research · 2025Article
- Risk factors for tuberculosis treatment outcomes: a statistical learning-based exploration using the SINAN database with incomplete observations.BMC medical informatics and decision making · 2025Article
- Explainable machine learning for early predicting treatment failure risk among patients with TB-diabetes comorbidity.Scientific reports · 2024Article
- Integrative analysis of multimodal patient data identifies personalized predictors of tuberculosis treatment prognosis.iScience · 2024Article
- A machine learning-based prediction of hospital mortality in mechanically ventilated ICU patients.PloS one · 2024Article
- Deep learning on longitudinal CT scans: automated prediction of treatment outcomes in hospitalized tuberculosis patients.iScience · 2023Article
- Development of prognostic scoring system for predicting 1-year mortality among pulmonary tuberculosis patients in South India.Journal of public health (Oxford, England) · 2023Observational
- Effect of treatment adherence on the association between sex and unfavourable treatment outcomes among tuberculosis patients in Puducherry, India: a mediation analysis.Journal of public health (Oxford, England) · 2023Observational
- Using an Artificial Intelligence Approach to Predict the Adverse Effects and Prognosis of Tuberculosis.Diagnostics (Basel, Switzerland) · 2023Article
- A nomogram for predicting mortality of patients initially diagnosed with primary pulmonary tuberculosis in Hunan province, China: a retrospective study.Frontiers in cellular and infection microbiology · 2023Article
- A machine learning approach to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district.PLOS global public health · 2023Article
- Utility of Machine Learning and Radiomics Based on Cavity for Predicting the Therapeutic Response of MDR-TB.Infection and drug resistance · 2023Article
- Integrative analysis of clinical health records, imaging and pathogen genomics identifies personalized predictors of disease prognosis in tuberculosis.medRxiv : the preprint server for health sciences · 2022Article
- Risk factors of lymph node metastasis or lymphovascular invasion for superficial esophageal squamous cell carcinoma: A practical and effective predictive nomogram based on a cancer hospital data.Frontiers in medicine · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTuberculosis 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.
objectiveTo identify features associated with treatment failure and to predict which patients are at highest risk of treatment failure.
methodsOn 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.
resultsThe 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.
conclusionMachine 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.
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