Machine learning algorithms using national registry data to predict loss to follow-up during tuberculosis treatment

• Päätekijät: Moreno M. S. Rodrigues (Tekijä), Beatriz Barreto-Duarte (Tekijä), Caian L. Vinhaes (Tekijä), Mariana Araújo-Pereira (Tekijä), Eduardo R. Fukutani (Tekijä), Keityane Bone Bergamaschi (Tekijä), Afrânio Kristki (Tekijä), Marcelo Cordeiro-Santos (Tekijä), Valeria C. Rolla (Tekijä), Timothy R. Sterling (Tekijä), Artur T. L. Queiroz (Tekijä), Bruno B. Andrade (Tekijä)
• Aineistotyyppi: Kirja
• Julkaistu: BMC, 2024-05-01T00:00:00Z.
• Aiheet: article
• Linkit: Connect to this object online.

Abstract 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 national registry data in prediction models may be a useful tool to inform healthcare workers about risk of LTFU...