Bacterial load slopes represent biomarkers of tuberculosis therapy success, failure, and relapse
Magombedze et al. propose a new method combining mathematical modeling and machine learning to derive early (within 2 months) effective predictive biomarkers from bacterial load slopes for tuberculosis long term treatment outcomes, thereby accurately predicting treatment failure and success.
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Autores principales: | , , |
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Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
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Materias: | |
Acceso en línea: | https://doaj.org/article/f6084c5fc4754ebaa58c73fa91449396 |
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Sumario: | Magombedze et al. propose a new method combining mathematical modeling and machine learning to derive early (within 2 months) effective predictive biomarkers from bacterial load slopes for tuberculosis long term treatment outcomes, thereby accurately predicting treatment failure and success. |
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