Machine learning-based high-specificity diagnostic model for Talaromyces marneffei infection in febrile patients using routine clinical laboratory data
A random forest model can effectively utilize routine clinical laboratory data to predict <i>T. marneffei</i> infection and bacteraemia in febrile patients, offering a promising early screening tool for individuals at high risk for <i>T. marneffei</i> infection.
Authors
- Xiaomin Ou (ORCID: https://orcid.org/0000-0001-9162-1950)
- Zuoquan Dong
- Chuanhui Xu (ORCID: https://orcid.org/0000-0002-3222-7168)
- Wei Liang (ORCID: https://orcid.org/0000-0002-1098-5662)
- Xiling Chen (ORCID: https://orcid.org/0009-0007-4941-4705)
- Xinyu Nan (ORCID: https://orcid.org/0009-0007-8245-2607)
- Yingjun Xiao (ORCID: https://orcid.org/0000-0002-7009-9662)
- Kui Fang (ORCID: https://orcid.org/0000-0001-5646-6553)
- Xiaoyan Zhang (ORCID: https://orcid.org/0000-0002-8062-1127)
- Xiaobo Lai
- Peng Xu
Institutions
- Zhejiang Chinese Medical University (CN)
- Wenzhou Medical University (CN)
- First People's Hospital of Yuhang District (CN)
- Hangzhou Hospital of Traditional Chinese Medicine (CN)
- The Third Affiliated Hospital of Zhejiang Chinese Medical University (CN)
- Zhejiang Taizhou Hospital (CN)
Publication Details
- Journal
- Frontiers in Microbiology
- Published
- 2025-09-04
- DOI
- https://doi.org/10.3389/fmicb.2025.1654918
- Primary Topic
- Mycobacterium research and diagnosis
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Zhejiang Traditional Chinese Medicine Administration
- Natural Science Foundation of Zhejiang Province