Machine learning model predicts airway stenosis requiring clinical intervention in patients after lung transplantation: a retrospective case-controlled study.

Journal: BMC medical informatics and decision making
PMID: 39160522

Abstract

BACKGROUND: Patients with airway stenosis (AS) are associated with considerable morbidity and mortality after lung transplantation (LTx). This study aims to develop and validate machine learning (ML) models to predict AS requiring clinical intervention in patients after LTx.

Authors

  • Dong Tian
    Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China.
  • Yu-Jie Zuo
    Department of Clinical Medicine, North Sichuan Medical College, Nanchong, 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.
  • Ming-Zhao Liu
    Wuxi Lung Transplant Center, Wuxi People's Hospital affiliated to Nanjing Medical University, Wuxi, China.
  • Hang Yang
    Department of Clinical Research Center, Dazhou Central Hospital, Dazhou 635000, China.
  • Jin Zhao
    Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, 36 Lazi East Road, Tianfu New Area, Chengdu, 610000, 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.

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