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

Autor:	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, Bruno B. Andrade
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	Tuberculosis Score prediction Loss to follow-up Machine learning Public aspects of medicine RA1-1270
Zdroj:	BMC Public Health, Vol 24, Iss 1, Pp 1-9 (2024)
Druh dokumentu:	article
ISSN:	1471-2458
DOI:	10.1186/s12889-024-18815-0
Popis:	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 (
Databáze:	Directory of Open Access Journals
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