Prediction of long-term mortality by using machine learning models in Chinese patients with connective tissue disease-associated interstitial lung disease.

Journal: Respiratory research
Published Date: Jan 7, 2022

Abstract

BACKGROUND: The exact risk assessment is crucial for the management of connective tissue disease-associated interstitial lung disease (CTD-ILD) patients. In the present study, we develop a nomogram to predict 3‑ and 5-year mortality by using machine learning approach and test the ILD-GAP model in Chinese CTD-ILD patients.

Authors

  • Di Sun
    Department of Neurology, Neuroscience Center, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou 310016, China.
  • Yu Wang
    Clinical and Technical Support, Philips Healthcare, Shanghai, China.
  • Qing Liu
    School of Chemistry and Chemical Engineering, Shandong University of Technology, 255049, Zibo, PR China.
  • Tingting Wang
    Department of Anesthesiology, Taizhou Hospital, Linhai, China.
  • Pengfei Li
  • Tianci Jiang
    School of Mechanical Engineering, Waseda University, Kitakyushu 8080135, Japan.
  • Lingling Dai
    Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, The People's Republic of China.
  • Liuqun Jia
    Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, The People's Republic of China.
  • Wenjing Zhao
    Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, The People's Republic of China.
  • Zhe Cheng
    Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, The People's Republic of China. chengzhezzu@outlook.com.

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