Machine learning algorithms to predict treatment success for patients with pulmonary tuberculosis.

Despite advancements in detection and treatment, tuberculosis (TB), an infectious illness caused by the Mycobacterium TB bacteria, continues to pose a serious threat to world health. The TB diagnosis phase includes a patient's medical history, physical examination, chest X-rays, and laboratory procedures, such as molecular testing and sputum culture. In artificial intelligence (AI), machine learning (ML) is an advanced study of statistical algorithms that can learn from historical data and generalize the results to unseen data.

Bayesian structural equation modeling for post treatment health related quality of life among tuberculosis patients.

Background: The use of Bayesian Structural Equation Model (BSEM) to evaluate the impact of TB on self-reported health related quality of life (HRQoL) of TB patients has been not studied.

Objective: To identify the factors that contribute to the HRQoL of TB patients using BSEM.

Methods: This is a latent variable modeling with Bayesian approach using secondary data.

Strategies for smoking cessation (pharmacologic intervention versus enhanced motivation vs. standard motivation) in TB patients under treatment in the RNTCP, India - A cluster - Randomized trial.

Background: Tuberculosis burden is still high and smoking prevalence among males has increased in India. It is found that increased morbidity, mortality and relapse among TB smokers.

Method: Setting: Patients from two Revised National Tuberculosis Control Program Centres of Tamilnadu form the study population.

4-month moxifloxacin containing regimens in the treatment of patients with sputum-positive pulmonary tuberculosis in South India - a randomised clinical trial.

Background: Shortening tuberculosis (TB) treatment duration is a research priority. We tested the efficacy and safety of 3- and 4-month regimens containing moxifloxacin in a randomised clinical trial in pulmonary TB (PTB) patients in South India.

Methods: New, sputum-positive, adult, HIV-negative, non-diabetic PTB patients were randomised to 3- or 4-month moxifloxacin regimens [moxifloxacin (M), isoniazid (H), rifampicin (R), pyrazinamide (Z) and ethambutol (E)] or to a control regimen (2H R Z E /4R H ) [C].

Structural equation modeling of latent growth curves of weight gain among treated tuberculosis patients.

Tuberculosis still remains a major public health problem even though it is treatable and curable. Weight gain measurement during anti tuberculosis (TB) treatment period is an important component to assess the progress of TB patients. In this study, Latent Growth Models (LGMs) were implemented in a longitudinal design to predict the change in weight of TB patients who were given three different regimens under randomized controlled clinical trial for anti-TB treatment.