Predictive machine learning models for anticipating loss to follow-up in tuberculosis patients throu...
Predictive machine learning models for anticipating loss to follow-up in tuberculosis patients throughout anti-TB treatment journey
About this item
Full title
Author / Creator
Chen, Jingfang , Jiang, Youli , Li, Zhihuan , Zhang, Mingshu , Liu, Linlin , Li, Ao and Lu, Hongzhou
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
Formats
Publication information
Publisher
London: Nature Publishing Group UK
Subjects
More information
Scope and Contents
Contents
Loss to follow-up (LTFU) in tuberculosis (TB) management increases morbidity and mortality, challenging effective control strategies. This study aims to develop and evaluate machine learning models to predict loss to follow-up in TB patients, improving treatment adherence and outcomes. Retrospective data encompassing tuberculosis patients who under...
Alternative Titles
Full title
Predictive machine learning models for anticipating loss to follow-up in tuberculosis patients throughout anti-TB treatment journey
Authors, Artists and Contributors
Author / Creator
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Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_3a75d19059b5428393d341e14b3069bc
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_3a75d19059b5428393d341e14b3069bc
Other Identifiers
ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-024-74942-z
