Identifying severe community-acquired pneumonia using radiomics and clinical data: a machine learnin...
Identifying severe community-acquired pneumonia using radiomics and clinical data: a machine learning approach
About this item
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Author / Creator
Yang, Tianning , Zhang, Ling , Sun, Siyi , Yao, Xuexin , Wang, Lichuan and Ge, Yanlei
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
Formats
Publication information
Publisher
London: Nature Publishing Group UK
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Scope and Contents
Contents
Evaluating Community-Acquired Pneumonia (CAP) is crucial for determining appropriate treatment methods. In this study, we established a machine learning model using radiomics and clinical features to rapidly and accurately identify Severe Community-Acquired Pneumonia (SCAP). A total of 174 CAP patients were included in the study, with 64 cases clas...
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Full title
Identifying severe community-acquired pneumonia using radiomics and clinical data: a machine learning approach
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_e6bb66cccc474374a613e01b9a8320f2
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_e6bb66cccc474374a613e01b9a8320f2
Other Identifiers
ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-024-72310-5
