Development and validation of a predictive model for peripherally inserted central catheter-related thrombosis in breast cancer patients based on artificial neural network: A prospective cohort study.

Journal: International journal of nursing studies
PMID: 36084529

Abstract

BACKGROUND: Peripherally inserted central catheters have been extensively applied in clinical practices. However, they are associated with an increased risk of thrombosis. To improve patient care, it is critical to timely identify patients at risk of developing peripherally inserted central catheter-related thrombosis. Artificial neural networks have been successfully used in many areas of clinical events prediction and affected clinical decisions and practice.

Authors

  • Jianqin Fu
    Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China; Breast Cancer Institute, Fujian Medical University, Fuzhou, Fujian Province 350001, China.
  • Weifeng Cai
    Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China; Breast Cancer Institute, Fujian Medical University, Fuzhou, Fujian Province 350001, China.
  • Bangwei Zeng
    Administration Department of Nosocomial Infection, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China.
  • Lijuan He
    International Research Association on Emerging Automotive Safety Technology, Tianjin 300222, P. R. China.
  • Liqun Bao
    Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China; Breast Cancer Institute, Fujian Medical University, Fuzhou, Fujian Province 350001, China.
  • Zhaodi Lin
    Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China; Breast Cancer Institute, Fujian Medical University, Fuzhou, Fujian Province 350001, China.
  • Fang Lin
    State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin 300132, P.R.China;Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, School of Electrical Engineering, Hebei University of Technology, Tianjin 300132, P.R.China.
  • Wenjuan Hu
  • Linying Lin
    Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China; Breast Cancer Institute, Fujian Medical University, Fuzhou, Fujian Province 350001, China.
  • Hanying Huang
    Department of Nursing, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China.
  • Suhui Zheng
    Department of Nursing, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China.
  • Liyuan Chen
  • Wei Zhou
    Department of Eye Function Laboratory, Eye Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
  • Yanjuan Lin
    Department of Cardiovascular Surgery, Union Hospital, Fujian Medical University, No. 6, Xuefu South Road, Shangjie Town, Minhou County, 350108, Fuzhou, China. fjxhyjl@163.com.
  • Fangmeng Fu
    Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China; Breast Cancer Institute, Fujian Medical University, Fuzhou, Fujian Province 350001, China. Electronic address: ffm@fjmu.edu.cn.

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