Interpretable prognostic modeling for long-term survival of Type A aortic dissection patients using support vector machine algorithm.

Journal: European journal of medical research
PMID: 40229872

Abstract

OBJECTIVES: This study aims to develop a reliable and interpretable predictive model for long-term survival in Type A aortic dissection (TAAD) patients, utilizing machine learning (ML) algorithms.

Authors

  • Hao Cai
    Department of Nutrition and Food Hygiene, School of Public Health, Peking University, 38 Xue Yuan Road, Haidian District, Beijing 100191, China. caihao169@pku.edu.cn.
  • Yue Shao
    Department of Respiratory Medicine, Affiliated Zhongshan Hospital of Dalian University, Dalian, China.
  • Xuan-Yu Liu
    Department of Cardiothoracic Surgery, The First Affiliated Hospital of Chongqing Medical University, No.1, Medical College Road, Yuzhong District, Chongqing, 400016, China.
  • Chang-Ying Li
    Department of Cardiothoracic Surgery, The First Affiliated Hospital of Chongqing Medical University, No.1, Medical College Road, Yuzhong District, Chongqing, 400016, China.
  • Hao-Yu Ran
    Department of Cardiothoracic Surgery, The First Affiliated Hospital of Chongqing Medical University, No.1, Medical College Road, Yuzhong District, Chongqing, 400016, China.
  • Hao-Ming Shi
    Department of Cardiothoracic Surgery, The First Affiliated Hospital of Chongqing Medical University, No.1, Medical College Road, Yuzhong District, Chongqing, 400016, China.
  • Cheng Zhang
    College of Forestry, Jiangxi Agricultural University, Nanchang, Jiangxi Province, China.
  • Qing-Chen Wu
    Department of Cardiothoracic Surgery, The First Affiliated Hospital of Chongqing Medical University, No.1, Medical College Road, Yuzhong District, Chongqing, 400016, China. wuqingchencqmu@126.com.

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