A risk prediction model for venous thromboembolism in hospitalized patients with thoracic trauma: a machine learning, national multicenter retrospective study.

Journal: World journal of emergency surgery : WJES
PMID: 39948568

Abstract

BACKGROUND: Early treatment and prevention are the keys to reducing the mortality of VTE in patients with thoracic trauma. This study aimed to develop and validate an automatic prediction model based on machine learning for VTE risk screening in patients with thoracic trauma.

Authors

  • Kaibin Liu
    Department of Thoracic Surgery, Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital, Shanghai, 200233, China.
  • Di Qian
    Department of Health Statistics，Faculty of Health Service, Naval Medical University, 800 Xiangyin Road, Shanghai, 200433, China.
  • Dongsheng Zhang
    College of Radiology, Taishan Medical University, Taian 271016, China.
  • Zhichao Jin
    State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang, China.
  • Yi Yang
    Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Yanfang Zhao
    School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China.

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