ArticleRespiratory research2025
The large language model diagnoses tuberculous pleural effusion in pleural effusion patients through clinical feature landscapes.
Article in Respiratory research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Biochemistry-based machine learning algorithms in differentiating pleural effusion: current status and perspective.ERJ open research · 2026Review
- Development and validation of a simplified machine learning model based on T-SPOT.TB and routine clinical data for the diagnosis of tuberculous pleural effusion.Journal of thoracic disease · 2026Article
- Inflammatory, Nutritional, and Atherogenic Profiles Associated with Histologic Activity in Inflammatory Bowel Disease.Biomedicines · 2026Article
- Large Language Models in Infectious Diseases: A Systemic Review.Research square · 2026Article
- The Relationship Between Composite Inflammatory Indices and Dry Eye in Hashimoto's Disease-Induced Hypothyroid Patients.Biomedicines · 2025Article
- Machine learning-based Diagnostic model for determining the etiology of pleural effusion using Age, ADA and LDH.Respiratory research · 2025Article
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15 authors.
Funding
Abstract
backgroundTuberculous pleural effusion (TPE) is a challenging extrapulmonary manifestation of tuberculosis, with traditional diagnostic methods often involving invasive surgery and being time-consuming. While various machine learning and statistical models have been proposed for TPE diagnosis, these methods are typically limited by complexities in data processing and difficulties in feature integration. Therefore, this study aims to develop a diagnostic model for TPE using ChatGPT-4, a large language model (LLM), and compare its performance with traditional logistic regression and machine learning models. By highlighting the advantages of LLMs in handling complex clinical data, identifying interrelationships between features, and improving diagnostic accuracy, this study seeks to provide a more efficient and precise solution for the early diagnosis of TPE.
methodsWe conducted a cross-sectional study, collecting clinical data from 109 TPE and 54 non-TPE patients for analysis, selecting 73 features from over 600 initial variables. The performance of the LLM was compared with logistic regression and machine learning models (k-Nearest Neighbors, Random Forest, Support Vector Machines) using metrics like area under the curve (AUC), F1 score, sensitivity, and specificity.
resultsThe LLM showed comparable performance to machine learning models, outperforming logistic regression in sensitivity, specificity, and overall diagnostic accuracy. Key features such as adenosine deaminase (ADA) levels and monocyte percentage were effectively integrated into the model. We also developed a Python package ( https://pypi.org/project/tpeai/ ) for rapid TPE diagnosis based on clinical data.
conclusionsThe 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.
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