The use of machine and deep learning to model the relationship between discomfort temperature and labor productivity loss among petrochemical workers.

Journal: BMC public health
PMID: 39587532

Abstract

BACKGROUND: Workplace may not only increase the risk of heat-related illnesses and injuries but also compromise work efficiency, particularly in a warming climate. This study aimed to utilize machine learning (ML) and deep learning (DL) algorithms to quantify the impact of temperature discomfort on productivity loss among petrochemical workers and to identify key influencing factors.

Authors

  • Yilin Zhang
    Department of Preventive Medicine, School of Public Health, Fujian Medical University; and Key Laboratory of Environment and Health, Fujian Province University, 1 North Xue-Fu Rd, Minhou, Fuzhou, 350122, Fujian Province, China.
  • Yifeng Chen
    College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China.
  • Qingling Su
    Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, 350122, Fujian Province, China.
  • Xiaoyin Huang
    Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, 350122, Fujian Province, China.
  • Qingyu Li
    School of Mathematics, Physics and Data Science, Chongqing University of Science and Technology, Chongqing 401331, China.
  • Yan Yang
    Department of Endocrinology and Metabolism, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
  • Zitong Zhang
    The Second Clinical College, Chongqing Medical University, Chongqing, China.
  • Jiake Chen
    Department of Preventive Medicine, School of Public Health, Fujian Medical University; and Key Laboratory of Environment and Health, Fujian Province University, 1 North Xue-Fu Rd, Minhou, Fuzhou, 350122, Fujian Province, China.
  • Zhihong Xiao
    Department of Preventive Medicine, School of Public Health, Fujian Medical University; and Key Laboratory of Environment and Health, Fujian Province University, 1 North Xue-Fu Rd, Minhou, Fuzhou, 350122, Fujian Province, China.
  • Rong Xu
  • Qing Zu
    Minnan Branch of the First Affiliated Hospital of Fujian Medical University, Quangang, Quanzhou, 362100, Fujian Province, China.
  • Shanshan Du
    Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, 350122, Fujian Province, China.
  • Wei Zheng
    School of Computer Engineering, Jinling Institute of Technology, Nanjing, 211169, China. zhengwei@jit.edu.cn.
  • Weimin Ye
    School of Public Health, Fujian Medical University, Fuzhou, China.
  • Jianjun Xiang
    Department of Preventive Medicine, School of Public Health, Fujian Medical University; and Key Laboratory of Environment and Health, Fujian Province University, 1 North Xue-Fu Rd, Minhou, Fuzhou, 350122, Fujian Province, China. jianjun.xiang@fjmu.edu.cn.

Keywords

External Resources

Popular Topics
Recent Journals