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A novel multidisciplinary machine learning approach based on clinical, imaging, colonoscopy, and pathology features for distinguishing intestinal tuberculosis from Crohn's disease.
Baolan Lu et al. Abdom Radiol (NY) 2024
Explainable machine learning for early predicting treatment failure risk among patients with TB-diabetes comorbidity.
An-Zhou Peng et al. Sci Rep 2024 14(1) 6814
Integrative analysis of multimodal patient data identifies personalized predictors of tuberculosis treatment prognosis.
Awanti Sambarey et al. iScience 2024 27(2) 109025
Opportunities and limitations of genomics for diagnosing bedaquiline-resistant tuberculosis: a systematic review and individual isolate meta-analysis
C Nimmo et al, Lancet Microbe, January 9, 2024
Distinguishing infectivity in patients with pulmonary tuberculosis using deep learning.
Yi Gao et al. Front Public Health 2023 111247141
High-Throughput Phenotypic Screening and Machine Learning Methods Enabled the Selection of Broad-Spectrum Low-Toxicity Antitrypanosomatidic Agents.
Pasquale Linciano et al. J Med Chem 2023
Development and validation of a diagnostic model to differentiate spinal tuberculosis from pyogenic spondylitis by combining multiple machine learning algorithms.
Chengqian Huang et al. Biomol Biomed 2023
Current challenges and potential solutions to the use of digital health technologies in evidence generation: a narrative review.
Hassan Mumtaz et al. Front Digit Health 2023 51203945
Comparison of logistic regression with regularized machine learning methods for the prediction of tuberculosis disease in people living with HIV: cross-sectional hospital- based study in Kisumu County, Kenya.
James Orwa et al. Res Sq 2023
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Disclaimer: Articles listed in the Public Health Knowledge Base are selected by Public Health Genomics Branch to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.