Applying a Smartwatch to Predict Work-related Fatigue for Emergency Healthcare Professionals: Machine Learning Method.

Journal: The western journal of emergency medicine
Published Date: Jul 7, 2023

Abstract

INTRODUCTION: Healthcare professionals frequently experience work-related fatigue, which may jeopardize their health and put patient safety at risk. In this study, we applied a machine learning (ML) approach based on data collected from a smartwatch to construct prediction models of work-related fatigue for emergency clinicians.

Authors

  • Sot Shih-Hung Liu
    National Taiwan University Hospital, Department of Emergency Medicine, Taipei, Taiwan.
  • Cheng-Jiun Ma
    MOST Joint Research Center for AI Technology and All VISTA Healthcare (AINTU), Taipei, Taiwan.
  • Fan-Ya Chou
    National Taiwan University Hospital, Department of Emergency Medicine, Taipei, Taiwan.
  • Michelle Yuan-Chiao Cheng
    MOST Joint Research Center for AI Technology and All VISTA Healthcare (AINTU), Taipei, Taiwan.
  • Chih-Hung Wang
    National Taiwan University Hospital, Department of Emergency Medicine, Taipei, Taiwan.
  • Chu-Lin Tsai
    National Taiwan University Hospital, Department of Emergency Medicine, Taipei, Taiwan.
  • Wei-Jou Duh
    MOST Joint Research Center for AI Technology and All VISTA Healthcare (AINTU), Taipei, Taiwan.
  • Chien-Hua Huang
    Department of Emergency Medicine, National Taiwan University, 100 Taipei, Taiwan.
  • Feipei Lai
    Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Room 410, Barry Lam Hall, No.1, Sec.4, Roosevelt Road, Taipei, 10617, Taiwan, Republic of China.
  • Tsung-Chien Lu
    Department of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan.

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