Construction of prediction models for novel subtypes in patients with arteriosclerosis obliterans undergoing endovascular therapy: an unsupervised machine learning study.

Journal: Journal of cardiothoracic surgery
Published Date: Jun 25, 2024

Abstract

BACKGROUND: Arteriosclerosis obliterans (ASO) is a chronic arterial disease that can lead to critical limb ischemia. Endovascular therapy is increasingly used for limb salvage in ASO patients, but the outcomes vary. The development of prediction models using unsupervised machine learning may lead to the identification of novel subtypes to guide patient prognosis and treatment.

Authors

  • Xiaocheng Li
    Laboratory for Functional Glycomics, College of Life Sciences, Northwest University, Xi'an 710069, China.
  • Lin Zhang
    Laboratory of Molecular Translational Medicine, Centre for Translational Medicine, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, Clinical Research Center for Birth Defects of Sichuan Province, West China Second Hospital, Sichuan University, Chengdu, Sichuan, 610041, China. Electronic address: zhanglin@scu.edu.cn.
  • Que Li
    Department of Vascular Surgery Ward, The First Affiliated Hospital of Guangxi Medical University, No.6 of Shuangyong Road, Nanning, Guangxi, 530021, P. R. China.
  • Jiangfeng Zhang
    Department of Vascular Surgery Ward, The First Affiliated Hospital of Guangxi Medical University, No.6 of Shuangyong Road, Nanning, Guangxi, 530021, P. R. China.
  • Xiao Qin
    Worcester Polytechnic Institute, 100 Institute Rd, Worcester, MA, 01609, USA.

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