Enhancing prediction and stratifying risk: machine learning and bayesian-learning models for catheter-related thrombosis in chemotherapy patients.

Journal: BMC cancer
PMID: 40148861

Abstract

BACKGROUND: Catheter-related thrombosis (CRT) is a serious complication in cancer patients undergoing chemotherapy, yet existing risk prediction models demonstrate limited accuracy. This study aimed to evaluate the clinical utility of machine learning (ML) and Bayesian-learning models for CRT prediction in a large cohort of breast cancer patients undergoing catheterization.

Authors

  • Tao An
    Shanghai Astronomical Observatory, Key Laboratory of Radio Astronomy, Chinese Academy of Sciences, Shanghai 200030, China. Electronic address: antao@shao.ac.cn.
  • Han Han
    Department of Cardiac Surgery, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Junying Xie
    Department of Management Center, Cancer Hospital of Huanxing Chaoyang District, Beijing, China.
  • Yifan Wang
    School of Bioscience and Bioengineering, South China University of Technology, Guangzhou, China.
  • Yiqi Zhao
    Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.
  • Hao Jia
    School of Environmental Science and Engineering, Shaanxi University of Science and Technology, Xi'an 170021, China.
  • Yanfeng Wang

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