ArticleScientific reports2024
Explainable machine learning for early predicting treatment failure risk among patients with TB-diabetes comorbidity.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Revolutionizing diagnosis of pulmonaryFrontiers in microbiology · 2024Pooled it
- Predicting unfavorable tuberculosis outcomes using machine learning: a prospective cohort.Tropical medicine and health · 2026Article
- Integrated multi-omics profiling reveals immune-related biomarkers and regulatory networks for early prediction of tuberculosis in type 2 diabetes mellitus.Frontiers in immunology · 2026Article
- Identification of risk factors for latent tuberculosis infection in Xinjiang using machine learning.BMC public health · 2025Article
- Explainable machine learning for predicting clinical outcomes in HIV/TB co-infection: a comparative retrospective study.BMC infectious diseases · 2025Article
- Development of a machine learning model for early pulmonary tuberculosis diagnosis using blood test biomarkers.BMC infectious diseases · 2025Article
- A comparative study on TB incidence and HIVTB coinfection using machine learning models on WHO global TB dataset.Scientific reports · 2025Article
- A DNA methylation signature identified in the buccal mucosa reflecting active tuberculosis is changing during tuberculosis treatment.Scientific reports · 2024Article
- Artificial intelligence in tuberculosis: a new ally in disease control.Breathe (Sheffield, England) · 2024Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The present study aims to assess the treatment outcome of patients with diabetes and tuberculosis (TB-DM) at an early stage using machine learning (ML) based on electronic medical records (EMRs). A total of 429 patients were included at Chongqing Public Health Medical Center. The random-forest-based Boruta algorithm was employed to select the essential variables, and four models with a fivefold cross-validation scheme were used for modeling and model evaluation. Furthermore, we adopted SHapley additive explanations to interpret results from the tree-based model. 9 features out of 69 candidate features were chosen as predictors. Among these predictors, the type of resistance was the most important feature, followed by activated partial throm-boplastic time (APTT), thrombin time (TT), platelet distribution width (PDW), and prothrombin time (PT). All the models we established performed above an AUC 0.7 with good predictive performance. XGBoost, the optimal performing model, predicts the risk of treatment failure in the test set with an AUC 0.9281. This study suggests that machine learning approach (XGBoost) presented in this study identifies patients with TB-DM at higher risk of treatment failure at an early stage based on EMRs. The application of a convenient and economy EMRs based on machine learning provides new insight into TB-DM treatment strategies in low and middle-income countries.
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