Development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation.

Journal: Respiratory research
Published Date: Mar 31, 2021

Abstract

BACKGROUND: Pneumonia is the most frequently encountered postoperative pulmonary complications (PPC) after orthotopic liver transplantation (OLT), which cause high morbidity and mortality rates. We aimed to develop a model to predict postoperative pneumonia in OLT patients using machine learning (ML) methods.

Authors

  • Chaojin Chen
    Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, 10630, Guangdong, People's Republic of China.
  • Dong Yang
    College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology Xi'an 710021 China yangdong@sust.edu.cn.
  • Shilong Gao
    Department of Information, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China.
  • Yihan Zhang
    Department of Ophthalmology, Fudan Eye & ENT Hospital, Shanghai, China.
  • Liubing Chen
    Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, 10630, Guangdong, People's Republic of China.
  • Bohan Wang
    Hebei Key Laboratory of Water Quality Engineering and Comprehensive Utilization of Water Resources, Hebei University of Architecture, Zhangjiakou 075000, China.
  • Zihan Mo
    Guangzhou AID Cloud Technology Co., LTD, Guangzhou, People's Republic of China.
  • Yang Yang
    Department of Gastrointestinal Surgery, The Third Hospital of Hebei Medical University, Shijiazhuang, China.
  • Ziqing Hei
    Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, 10630, Guangdong, People's Republic of China. heiziqing@sina.com.
  • Shaoli Zhou
    Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, 10630, Guangdong, People's Republic of China. 13610272308@139.com.

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