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

Journal: Respiratory research
PMID: 39939874

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 provide a more efficient and precise solution for the early diagnosis of TPE.

Authors

  • Chaoling Wu
    Department of Respiratory Medicine, Affiliated Hospital of Jiujiang University, Jiujiang, China.
  • Wanyi Liu
    Department of Hematology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, China.
  • Pengfei Mei
    Department of Gastroenterology, Affiliated Hospital of Jiujiang University, Jiujiang, 332000, China.
  • Yunyun Liu
    Department of Respiratory Medicine, Affiliated Hospital of Jiujiang University, No. 57 East Xunyang Road, Xunyang District, Jiujiang, 332000, China.
  • Jian Cai
    Department of Colorectal Surgery, Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Department of Radiology, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China.
  • Lu Liu
    College of Pharmacy, Harbin Medical University, Harbin, China.
  • Juan Wang
    Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China.
  • Xuefeng Ling
    Department of Surgery, Stanford University, Stanford, CA, United States.
  • Mingxue Wang
    Department of Respiratory Medicine, Affiliated Hospital of Jiujiang University, No. 57 East Xunyang Road, Xunyang District, Jiujiang, 332000, China.
  • Yuanyuan Cheng
    Department of Respiratory Medicine, Affiliated Hospital of Jiujiang University, No. 57 East Xunyang Road, Xunyang District, Jiujiang, 332000, China.
  • Manbi He
    Department of Respiratory Medicine, Affiliated Hospital of Jiujiang University, No. 57 East Xunyang Road, Xunyang District, Jiujiang, 332000, China.
  • Qin He
    Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, Med-X Center for Materials, West China School of Pharmacy, Sichuan University, Chengdu, 610041, China. Electronic address: qinhe@scu.edu.cn.
  • Qi He
    Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, China.
  • Xiaoliang Yuan
    Department of Respiratory Medicine, First Affiliated Hospital of Gannan Medical University, No. 23, Qingnian Road, Zhanggong District, Ganzhou, 341000, China. yxlyyxs@126.com.
  • Jianlin Tong
    Department of Respiratory Medicine, Affiliated Hospital of Jiujiang University, No. 57 East Xunyang Road, Xunyang District, Jiujiang, 332000, China. tjl8880@163.com.

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