Integrating landmark modeling framework and machine learning algorithms for dynamic prediction of tuberculosis treatment outcomes
Objective This study aims to establish an informative dynamic prediction model of treatment
outcomes using follow-up records of tuberculosis (TB) patients, which can timely detect …
outcomes using follow-up records of tuberculosis (TB) patients, which can timely detect …
[HTML][HTML] Machine learning algorithms using national registry data to predict loss to follow-up during tuberculosis treatment
MMS Rodrigues, B Barreto-Duarte, CL Vinhaes… - BMC Public Health, 2024 - Springer
Background Identifying patients at increased risk of loss to follow-up (LTFU) is key to
developing strategies to optimize the clinical management of tuberculosis (TB). The use of …
developing strategies to optimize the clinical management of tuberculosis (TB). The use of …
A clinical prediction model for unsuccessful pulmonary tuberculosis treatment outcomes
LS Peetluk, PF Rebeiro, FM Ridolfi… - Clinical Infectious …, 2022 - academic.oup.com
Background Despite widespread availability of curative therapy, tuberculosis (TB) treatment
outcomes remain suboptimal. Clinical prediction models can inform treatment strategies to …
outcomes remain suboptimal. Clinical prediction models can inform treatment strategies to …
Development of CART model for prediction of tuberculosis treatment loss to follow up in the state of São Paulo, Brazil: A case–control study
Background Tuberculosis is the leading cause of infectious disease-related death,
surpassing even the immunodeficiency virus. Treatment loss to follow up and irregular …
surpassing even the immunodeficiency virus. Treatment loss to follow up and irregular …
Optimized support vector regression predicting treatment duration among tuberculosis patients in Malaysia
V Balakrishnan, G Ramanathan, S Zhou… - Multimedia Tools and …, 2024 - Springer
Abstract Machine learning models have emerged as an advanced tool for predicting
diseases and their outcomes. This study developed a machine learning model to predict the …
diseases and their outcomes. This study developed a machine learning model to predict the …
Investigating machine learning methods for tuberculosis risk factors prediction: a comparative analysis and evaluation
Tuberculosis (TB) is a killer disease, and its root can be traced to Mycobacterium
tuberculosis. As the world population increases, the burden of tuberculosis is growing along …
tuberculosis. As the world population increases, the burden of tuberculosis is growing along …
[HTML][HTML] Towards probabilistic decision support in public health practice: Predicting recent transmission of tuberculosis from patient attributes
Objective Investigating the contacts of a newly diagnosed tuberculosis (TB) case to prevent
TB transmission is a core public health activity. In the context of limited resources, it is often …
TB transmission is a core public health activity. In the context of limited resources, it is often …
A machine learning-based framework for Predicting Treatment Failure in tuberculosis: A case study of six countries
Tuberculosis is ranked as the 2nd deadliest disease in the world and is responsible for ten
million deaths in 2017. Treatment failure is one of a main reason behind these deaths …
million deaths in 2017. Treatment failure is one of a main reason behind these deaths …
Predicting treatment outcome of drug-susceptible tuberculosis patients using machine-learning models
OA Hussain, KN Junejo - Informatics for Health and Social Care, 2019 - Taylor & Francis
Tuberculosis (TB) is a deadly contagious disease and a serious global health problem. It is
curable but due to its lengthy treatment process, a patient is likely to leave the treatment …
curable but due to its lengthy treatment process, a patient is likely to leave the treatment …
Evaluation and comparison of different machine learning methods to predict outcome of tuberculosis treatment course
SRN Kalhori, XJ Zeng - Journal of Intelligent Learning …, 2013 - info.openarchivespress.com
Tuberculosis treatment course completion is crucial to protect patients against prolonged
infectiousness, relapse, lengthened and more expensive therapy due to multidrug …
infectiousness, relapse, lengthened and more expensive therapy due to multidrug …