Validation and interpretation of machine-learning models for rapid identification of active tuberculosis infection using routine laboratory indicators
The study presents a high-performing and interpretable machine learning model capable of accurately identifying active Mtb infection using routine blood tests. This low-cost and non-invasive approach has strong potential for application in resource-limited and high-burden settings.
Authors
- Jian K. Liu (ORCID: https://orcid.org/0000-0002-5391-7213)
- Jiawei Yan (ORCID: https://orcid.org/0000-0003-2978-5799)
- Zhan-Zhong Liu
- Quan Yuan (ORCID: https://orcid.org/0000-0002-8397-4024)
- Hui-Jin Chen
- Xue-Di Zhang
- Yudong Zhang (ORCID: https://orcid.org/0000-0002-4870-1493)
- Liang Wang (ORCID: https://orcid.org/0000-0001-5339-7484)
- Ke Du (ORCID: https://orcid.org/0000-0002-1560-4902)
Institutions
- Jiangsu University (CN)
- Xuzhou Medical College (CN)
- Xuzhou Cancer Hospital (CN)
- Shengli Oilfield Central Hospital (CN)
- Xuzhou No.1 People's Hospital (CN)
- Guangdong Academy of Medical Sciences (CN)
Publication Details
- Journal
- Frontiers in Cellular and Infection Microbiology
- Published
- 2025-12-18
- DOI
- https://doi.org/10.3389/fcimb.2025.1718614
- Primary Topic
- Tuberculosis Research and Epidemiology
- Type
- article
- Field-Weighted Citation Impact
- 0.00