Ten Machine Learning Models for Predicting Preoperative and Postoperative Coagulopathy in Patients With Trauma: Multicenter Cohort Study.

Journal: Journal of medical Internet research
PMID: 39841523

Abstract

BACKGROUND: Recent research has revealed the potential value of machine learning (ML) models in improving prognostic prediction for patients with trauma. ML can enhance predictions and identify which factors contribute the most to posttraumatic mortality. However, no studies have explored the risk factors, complications, and risk prediction of preoperative and postoperative traumatic coagulopathy (PPTIC) in patients with trauma.

Authors

  • Xiaojuan Xiong
    Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, China.
  • Hong Fu
    Department of Computer Science, Chu Hai College of Higher Education, 80 Castle Peak Road, Castle Peak Bay, Tuen Mun, NT, Hong Kong.
  • Bo Xu
    State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China.
  • Wang Wei
    School of Mechatronic Engineering and Automation, Shanghai University, 99 Shangda Road, BaoShan District, Shanghai 200444, China.
  • Mi Zhou
    The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangzhou, China.
  • Peng Hu
    The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
  • Yunqin Ren
    Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, China.
  • Qingxiang Mao
    Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, China.

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