Utilizing Machine Learning Techniques to Predict Negative Remodeling in Uncomplicated Type B Intramural Hematoma.

Journal: Annals of vascular surgery
PMID: 40054615

Abstract

BACKGROUND: To evaluate the effectiveness of machine learning (ML) techniques in predicting negative remodeling in uncomplicated Stanford type B intramural hematoma (IMHB) during the acute phase.

Authors

  • Qu Chen
    Department of Cardiovascular Surgery, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian Province, People's Republic of China.
  • Yuanyuan Jiang
    Department of Cardiovascular Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
  • Feng Kuang
    Department of Cardiovascular Surgery, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian Province, People's Republic of China.
  • Zhonggui Shan
    Department of Cardiovascular Surgery, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian Province, People's Republic of China. Electronic address: xmchenqu@163.com.

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