Development of a multi-laboratory integrated predictive model for silicosis utilizing machine learning: a retrospective case-control study.

Journal: Frontiers in public health
PMID: 39882130

Abstract

OBJECTIVE: Due to the high global prevalence of silicosis and the ongoing challenges in its diagnosis, this pilot study aims to screen biomarkers from routine blood parameters and develop a multi-biomarker model for its early detection.

Authors

  • Guo-Kang Sun
    Department of Laboratory, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
  • Yun-Hui Xiang
    Sichuan International Travel Health Care Center (Chengdu Customs Port Outpatient Department), Chengdu, China.
  • Lu Wang
    Department of Laboratory, Akesu Center of Disease Control and Prevention, Akesu, China.
  • Pin-Pin Xiang
    Department of Laboratory, Xiping Community Healthcare Center of Longquanyi District, Chengdu, China.
  • Zi-Xin Wang
    Department of Laboratory, Wangjiang Hospital, Sichuan University, Chengdu, China.
  • Jing Zhang
    MOEMIL Laboratory, School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu, China.
  • Ling Wu
    Department of Laboratory, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.

Keywords

External Resources

Popular Topics
Recent Journals