Machine learning-based models for advanced fibrosis in non-alcoholic steatohepatitis patients: A cohort study.

Journal: World journal of gastroenterology
PMID:

Abstract

BACKGROUND: The global prevalence of non-alcoholic steatohepatitis (NASH) and its associated risk of adverse outcomes, particularly in patients with advanced liver fibrosis, underscores the importance of early and accurate diagnosis.

Authors

  • Fei-Xiang Xiong
    Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China.
  • Lei Sun
    1Department of Biological Engineering, Utah State University, 4105 Old Main Hill, Logan, UT 84322-4105 USA.
  • Xue-Jie Zhang
    Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China.
  • Jia-Liang Chen
    Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China.
  • Yang Zhou
    State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Environmental Protection, Guangzhou, China.
  • Xiao-Min Ji
    Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China.
  • Pei-Pei Meng
    Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China.
  • Tong Wu
    National Clinical Research Center for Obstetrical and Gynecological Diseases Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology Wuhan China.
  • Xian-Bo Wang
    Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China. wangxb@ccmu.edu.cn.
  • Yi-Xin Hou
    Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China. xuexin162@163.com.