1. Machine learning algorithms using national registry data to predict loss to follow-up during tuberculosis treatment
- Author
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Moreno M. S. Rodrigues, Beatriz Barreto-Duarte, Caian L. Vinhaes, Mariana Araújo-Pereira, Eduardo R. Fukutani, Keityane Bone Bergamaschi, Afrânio Kristki, Marcelo Cordeiro-Santos, Valeria C. Rolla, Timothy R. Sterling, Artur T. L. Queiroz, and Bruno B. Andrade
- Subjects
Tuberculosis ,Score prediction ,Loss to follow-up ,Machine learning ,Public aspects of medicine ,RA1-1270 - Abstract
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. Here we developed a score to predict the risk of LTFU during anti-TB treatment (ATT) in a nationwide cohort of cases using clinical data reported to the Brazilian Notifiable Disease Information System (SINAN). Methods We performed a retrospective study of all TB cases reported to SINAN between 2015 and 2022; excluding children (
- Published
- 2024
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