AIMC Topic: Tuberculosis
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Risk Prediction of Liver Injury in Pediatric Tuberculosis Treatment: Development of an Automated Machine Learning Model.
Drug design, development and therapy
Jan 13, 2025
PURPOSE: Drug-induced liver injury (DILI) is one of the most common and serious adverse drug reactions related to first-line anti-tuberculosis drugs in pediatric tuberculosis patients. This study aims to develop an automatic machine learning (AutoML)...
Enhanced swin transformer based tuberculosis classification with segmentation using chest X-ray.
Journal of X-ray science and technology
Jan 8, 2025
BACKGROUND:: Tuberculosis disease is the disease that causes significant morbidity and mortality worldwide. Thus, early detection of the disease is crucial for proper treatment and controlling the spread of Tuberculosis disease. Chest X-ray imaging i...
Predicting the risk of pulmonary embolism in patients with tuberculosis using machine learning algorithms.
European journal of medical research
Dec 23, 2024
BACKGROUND: This study aimed to develop predictive models with robust generalization capabilities for assessing the risk of pulmonary embolism in patients with tuberculosis using machine learning algorithms.
Diagnostic Accuracy of Artificial Intelligence-Based Chest X-Ray reading for screening of Tuberculosis.
Journal of Nepal Health Research Council
Dec 19, 2024
BACKGROUND: Tuberculosis remains a public health challenge in Nepal and ranks as the seventh leading cause of death in the country. The END Tuberculosis strategy stresses - the screening for symptoms alone may not suffice; additional screening tools ...
Hyb_SEnc: An Antituberculosis Peptide Predictor Based on a Hybrid Feature Vector and Stacked Ensemble Learning.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 10, 2024
Tuberculosis has plagued mankind since ancient times, and the struggle between humans and tuberculosis continues. Mycobacterium tuberculosis is the leading cause of tuberculosis, infecting nearly one-third of the world's population. The rise of pepti...
Identification of diagnostic biomarkers and molecular subtype analysis associated with m6A in Tuberculosis immunopathology using machine learning.
Scientific reports
Dec 2, 2024
Tuberculosis (TB), ranking just below COVID-19 in global mortality, is a highly complex infectious disease involving intricate immunological molecules, diverse signaling pathways, and multifaceted immune processes. N6-methyladenosine (m6A), a critica...
Integrated machine learning and physics-based methods assisted de novo design of Fatty Acyl-CoA synthase inhibitors.
Expert opinion on drug discovery
Nov 25, 2024
BACKGROUND: Tuberculosis is an infectious disease that has become endemic worldwide. The causative bacteria (Mtb) is targeted via several exciting drug targets. One newly discovered target is the Fatty Acyl-CoA synthase, which plays a significant ro...
A comprehensive study on tuberculosis prediction models: Integrating machine learning into epidemiological analysis.
Journal of theoretical biology
Nov 13, 2024
Tuberculosis (TB), the second leading infectious killer globally, claimed the lives of 1.3 million individuals in 2022, after COVID-19, surpassing the toll of HIV and AIDS. With an estimated 10.6 million new TB cases worldwide in 2022, the gravity of...
Combining bioinformatics and machine learning to identify diagnostic biomarkers of TB associated with immune cell infiltration.
Tuberculosis (Edinburgh, Scotland)
Oct 11, 2024
OBJECTIVE: The asymptomatic nature of tuberculosis (TB) during its latent phase, combined with limitations in current diagnostic methods, makes accurate diagnosis challenging. This study aims to identify TB diagnostic biomarkers by integrating gene e...
Regional and National Trends in Tuberculosis Research in South Asian Association for Regional Cooperation Countries: Post-COVID-19 Pandemic Machine Learning Factorial Analysis.
International journal of mycobacteriology
Sep 14, 2024
BACKGROUND: The number of tuberculosis (TB)-related morbidities and mortalities is still high in the South-east Asian region. This study was performed to characterize and visualize the post-coronavirus disease 2019 (COVID-19) TB research in South Asi...
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