Machine Learning-Based Prognostic Model for Patients After Lung Transplantation.

Journal: JAMA network open
PMID:

Abstract

IMPORTANCE: Although numerous prognostic factors have been found for patients after lung transplantation (LTx) over the years, an accurate prognostic tool for LTx recipients remains unavailable.

Authors

  • Dong Tian
    Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China.
  • Hao-Ji Yan
    Department of General Thoracic Surgery, Juntendo University School of Medicine, Tokyo, Japan.
  • Heng Huang
    Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, USA.
  • Yu-Jie Zuo
    Department of Clinical Medicine, North Sichuan Medical College, Nanchong, China.
  • Ming-Zhao Liu
    Wuxi Lung Transplant Center, Wuxi People's Hospital affiliated to Nanjing Medical University, Wuxi, China.
  • Jin Zhao
    Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, 36 Lazi East Road, Tianfu New Area, Chengdu, 610000, China.
  • Bo Wu
    Beijing National Laboratory for Molecular Sciences Institute of Chemistry Chinese Academy of Sciences Beijing China.
  • Ling-Zhi Shi
    Wuxi Lung Transplant Center, Wuxi People's Hospital affiliated to Nanjing Medical University, Wuxi, China.
  • Jing-Yu Chen
    Wuxi Lung Transplant Center, Wuxi People's Hospital affiliated to Nanjing Medical University, Wuxi, China.