Feature selection and prediction of treatment failure in tuberculosis Article Swipe
Christopher Martin Sauer
,
David Sasson
,
Kenneth Paik
,
Ned McCague
,
Leo Anthony Celi
,
Iván Sánchez Fernández
,
Ben Illigens
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0207491
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0207491
Machine learning can help to identify patients at higher risk of treatment failure. Closer monitoring of these patients may decrease treatment failure rates and prevent emergence of antibiotic resistance. The use of inexpensive basic demographic and clinical features makes this approach attractive in low and middle-income countries.
Related Topics
Concepts
Medicine
Tuberculosis
Extensively drug-resistant tuberculosis
Drug resistance
Feature selection
Intensive care medicine
Internal medicine
Machine learning
Mycobacterium tuberculosis
Pathology
Computer science
Biology
Microbiology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0207491
- https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0207491&type=printable
- OA Status
- gold
- Cited By
- 55
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2901847723
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W2901847723Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1371/journal.pone.0207491Digital Object Identifier
- Title
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Feature selection and prediction of treatment failure in tuberculosisWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2018Year of publication
- Publication date
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2018-11-20Full publication date if available
- Authors
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Christopher Martin Sauer, David Sasson, Kenneth Paik, Ned McCague, Leo Anthony Celi, Iván Sánchez Fernández, Ben IlligensList of authors in order
- Landing page
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https://doi.org/10.1371/journal.pone.0207491Publisher landing page
- PDF URL
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https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0207491&type=printableDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0207491&type=printableDirect OA link when available
- Concepts
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Medicine, Tuberculosis, Extensively drug-resistant tuberculosis, Drug resistance, Feature selection, Intensive care medicine, Internal medicine, Machine learning, Mycobacterium tuberculosis, Pathology, Computer science, Biology, MicrobiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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55Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 8, 2023: 12, 2022: 10, 2021: 10Per-year citation counts (last 5 years)
- References (count)
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25Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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