A machine-learning based model for automated recommendation of individualized treatment of rifampicin-resistant tuberculosis
Our findings suggest that global implementation of the novel treatment recommender CDSS holds the potential to improve treatment outcomes of patients with RR-TB, especially those with 'difficult-to-treat' forms of RR-TB.
Authors
- Kelly E. Dooley (ORCID: https://orcid.org/0000-0001-9013-5256)
- Robin M. Warren (ORCID: https://orcid.org/0000-0001-5741-7358)
- Steven Callens (ORCID: https://orcid.org/0000-0002-7245-527X)
- Kris Laukens (ORCID: https://orcid.org/0000-0002-8217-2564)
- Samantha Potgieter (ORCID: https://orcid.org/0000-0001-9115-3341)
- John Black (ORCID: https://orcid.org/0000-0002-7845-4972)
- Gary Maartens (ORCID: https://orcid.org/0000-0003-3080-6606)
- Annelies Van Rie (ORCID: https://orcid.org/0000-0001-7666-3263)
- Lennert Verboven (ORCID: https://orcid.org/0000-0002-5647-5852)
- Ruben Cartuyvels (ORCID: https://orcid.org/0000-0003-1063-4659)
Institutions
- South African Medical Research Council (ZA)
- University of Antwerp (BE)
- University of Cape Town (ZA)
- Stellenbosch University (ZA)
- University of the Free State (ZA)
- Ghent University Hospital (BE)
- Livingstone Hospital (ZA)
- South African Tuberculosis Vaccine Initiative (ZA)
- Vanderbilt University Medical Center (US)
- KU Leuven (BE)
Publication Details
- Journal
- PLoS ONE
- Published
- 2024-09-06
- DOI
- https://doi.org/10.1371/journal.pone.0306101
- Citations
- 5
- Primary Topic
- Tuberculosis Research and Epidemiology
- Type
- article
- Field-Weighted Citation Impact
- 1.95
Funders
- Division of Intramural Research, National Institute of Allergy and Infectious Diseases
- Fonds Wetenschappelijk Onderzoek
- Vlaamse regering
- National Institutes of Health
- National Institute of Allergy and Infectious Diseases