The large language model diagnoses tuberculous pleural effusion in pleural effusion patients through clinical feature landscapes
The LLM-based model offers a non-surgical, accurate, and cost-effective method for early TPE diagnosis. The Python package provides a user-friendly tool for clinicians, with potential for broader use. Further validation in larger datasets is needed to optimize the model for clinical application.
Authors
- Lu Liu (ORCID: https://orcid.org/0000-0003-1013-4507)
- Min He (ORCID: https://orcid.org/0000-0003-0361-8984)
- Xuefeng B. Ling (ORCID: https://orcid.org/0000-0002-5386-3884)
- Jianlin Tong (ORCID: https://orcid.org/0009-0002-6373-1022)
- Wanyi Liu (ORCID: https://orcid.org/0000-0002-9535-7461)
- Xiaoliang Yuan (ORCID: https://orcid.org/0000-0003-2479-0361)
- Peng-jin Mei
- Yuanyuan Cheng (ORCID: https://orcid.org/0000-0003-1105-0495)
- Juan Wang (ORCID: https://orcid.org/0000-0001-8724-9850)
- Yunyun Liu (ORCID: https://orcid.org/0000-0002-5553-1672)
- He Qin
- Qi He (ORCID: https://orcid.org/0009-0003-8093-9455)
- Chaoling Wu
- Jian Cai
- Mingxue Wang
Institutions
- Fujian Medical University (CN)
- Jiujiang University (CN)
- First Affiliated Hospital of Gannan Medical University (CN)
- Second Affiliated Hospital of Fujian Medical University (CN)
Publication Details
- Journal
- Respiratory Research
- Published
- 2025-02-12
- DOI
- https://doi.org/10.1186/s12931-025-03130-y
- Citations
- 4
- Primary Topic
- Pleural and Pulmonary Diseases
- Type
- article
- Field-Weighted Citation Impact
- 6.07
Funders
- Natural Science Foundation of Jiangxi Province
- Key Research and Development Program of Jiangxi Province
- Fujian Medical University