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

• Main Authors: Moreno M. S. Rodrigues (Author), Beatriz Barreto-Duarte (Author), Caian L. Vinhaes (Author), Mariana Araújo-Pereira (Author), Eduardo R. Fukutani (Author), Keityane Bone Bergamaschi (Author), Afrânio Kristki (Author), Marcelo Cordeiro-Santos (Author), Valeria C. Rolla (Author), Timothy R. Sterling (Author), Artur T. L. Queiroz (Author), Bruno B. Andrade (Author)
• Format: Book
• Published: BMC, 2024-05-01T00:00:00Z.
• Subjects: article
• Online Access: 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...