ArticleScientific reports2024
Early prediction of Mycobacterium tuberculosis transmission clusters using supervised learning models.
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 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Comorbidity of chronic obstructive pulmonary disease and pulmonary tuberculosis: a bibliometric analysis (2011-2025) with narrative review.Journal of thoracic disease · 2026Article
- Machine Learning-based Prediction of Active Tuberculosis in People With HIV Using Clinical Data.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2025Article
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Authors and funding
5 authors.
Funding
Abstract
Identifying individuals with tuberculosis (TB) with a high risk of onward transmission can guide disease prevention and public health strategies. Here, we train classification models to predict the first sampled isolates in Mycobacterium tuberculosis transmission clusters from demographic and disease data. We find that supervised learning, in particular balanced random forests, can be used to develop predictive models to identify people with TB that are more likely associated with TB cluster growth, with good model performance and AUCs of ≥ 0.75. We also identified the most important patient and disease characteristics in the best performing classification model, including host demographics, site of infection, TB lineage, and age at diagnosis. This framework can be used to develop predictive tools for the early assessment of potential cluster growth to prioritise individuals for enhanced follow-up with the aim of reducing transmission chains.
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