The large language model diagnoses tuberculous pleural effusion in pleural effusion patients through clinical feature landscapes.

ABSTRACT:

Background

Tuberculous 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

SUBMITTER: Wu C 

PROVIDER: S-EPMC11823098 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature