AIMC Topic: Antitubercular Agents
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Risk factors for tuberculosis treatment outcomes: a statistical learning-based exploration using the SINAN database with incomplete observations.
BMC medical informatics and decision making
Aug 11, 2025
BACKGROUND: Understanding early predictors of treatment outcomes allows better outcome prediction and resource allocation for efficient tuberculosis (TB) management.
Application of causal forest double machine learning (DML) approach to assess tuberculosis preventive therapy's impact on ART adherence.
Scientific reports
Aug 9, 2025
Adherence to antiretroviral therapy (ART) is critical for HIV treatment success, yet the impact of tuberculosis preventive therapy (TPT) remains inadequately understood. Using observational data from 4152 HIV patients in Ethiopia (2005-2024), we appl...
Machine learning-based prediction of antimicrobial resistance and identification of AMR-related SNPs in Mycobacterium tuberculosis.
BMC genomic data
Jul 12, 2025
BACKGROUND: Mycobacterium tuberculosis (MTB) is a human-specific pathogen that primarily infects humans, causing tuberculosis (TB). Antimicrobial resistance (AMR) in MTB presents a formidable challenge to global health. The employment of machine lear...
Enhanced diagnosis of multi-drug-resistant microbes using group association modeling and machine learning.
Nature communications
Mar 25, 2025
New solutions are needed to detect genotype-phenotype associations involved in microbial drug resistance. Herein, we describe a Group Association Model (GAM) that accurately identifies genetic variants linked to drug resistance and mitigates false-po...
Tuberculosis of the central nervous system: current concepts in diagnosis and treatment.
Current opinion in neurology
Mar 6, 2025
PURPOSE OF REVIEW: The outcome of central nervous system (CNS) tuberculosis has shown little improvement over several decades, with diagnosis remaining unconfirmed in nearly half of the cases. This review highlights current insights and advancements ...
Prediction of tuberculosis treatment outcomes using biochemical makers with machine learning.
BMC infectious diseases
Feb 17, 2025
BACKGROUND: Tuberculosis (TB) continues to pose a significant threat to global public health. Enhancing patient prognosis is essential for alleviating the disease burden.
Artificial Intuition and accelerating the process of antimicrobial drug discovery.
Computers in biology and medicine
Feb 14, 2025
New drug development is a very challenging, expensive, and usually time-consuming process. This issue is very important with regard to antimicrobials, which are affected by the global issue of the development and spread of resistance. This framework ...
Deep learning-driven bacterial cytological profiling to determine antimicrobial mechanisms in .
Proceedings of the National Academy of Sciences of the United States of America
Feb 6, 2025
Tuberculosis (TB), caused by , remains a significant global health threat, affecting an estimated 10.6 million people in 2022. The emergence of multidrug resistant and extensively drug resistant strains necessitates the development of novel and effec...
Machine learning model to predict the adherence of tuberculosis patients experiencing increased levels of liver enzymes in Indonesia.
PloS one
Jan 24, 2025
Indonesia is still the second-highest tuberculosis burden country in the world. The antituberculosis adverse drug reaction and adherence may influence the success of treatment. The objective of this study is to define the model for predicting the adh...
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)...
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