Interpretable noninvasive diagnosis of tuberculous pleural effusion using LGBM and SHAP: development and clinical application of a machine learning model
This interpretable LGBM-based model provides a reliable, noninvasive tool for TPE diagnosis. It supports clinical decision-making with real-time risk assessment and promises broader applicability through future integration into clinical information systems.
Authors
- Xingyu Yu (ORCID: https://orcid.org/0009-0009-5382-973X)
- Jijun Tong
- Lei Jiang (ORCID: https://orcid.org/0009-0007-1000-5752)
- Jian Wu (ORCID: https://orcid.org/0000-0002-3394-1507)
- Liannv Qiu (ORCID: https://orcid.org/0000-0003-3944-4509)
- Bihua Yao
- Shimeng Mao
- Er-min Gu (ORCID: https://orcid.org/0000-0001-6192-8695)
Institutions
- Zhejiang Chinese Medical University (CN)
- Zhejiang Sci-Tech University (CN)
- Jiaxing University (CN)
- Zhejiang Provincial People's Hospital (CN)
- First Hospital of Jiaxing (CN)
Publication Details
- Journal
- PeerJ
- Published
- 2025-05-20
- DOI
- https://doi.org/10.7717/peerj.19411
- Citations
- 1
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 1.36