Utilizing machine learning for predicting mortality in patients with heat-related illness who visited the emergency department.

Journal: International journal of medical informatics
Published Date: Apr 26, 2025

Abstract

BACKGROUND: In the context of climate change and global warming, heat-related illness (HRI) is anticipated to escalate and become a major concern. Patients with severe HRI primarily present to the emergency department (ED), but there are no prediction tools for mortality in HRI patients who visit ED. The objective of this study was to use machine learning approaches to establish prediction models for mortality in patients with HRI who visit ED.

Authors

  • Wan-Yin Kuo
    Department of Occupational Medicine, Chi Mei Medical Center, Tainan, Taiwan; Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Department of Emergency Medicine, Chi Mei Medical Center, Tainan, Taiwan.
  • Chien-Cheng Huang
    Department of Emergency Medicine, Chi Mei Medical Center, Tainan, Taiwan.
  • Chung-Feng Liu
    Department of Medical Research, Chi Mei Medical Center, 901 Zhonghua Road, Yongkang District, Tainan City, 710, Taiwan.
  • Mei-I Sung
    Department of Medical Research, Chi Mei Medical Center, Tainan, Taiwan.
  • Chien-Chin Hsu
    Department of Emergency Medicine, Chi Mei Medical Center, 901 Zhonghua Road, Yongkang District, Tainan City, 710, Taiwan.
  • Hung-Jung Lin
    Department of Emergency Medicine, Chi Mei Medical Center, 901 Zhonghua Road, Yongkang District, Tainan City, 710, Taiwan.
  • Shih-Bin Su
    Department of Occupational Medicine, Chi Mei Medical Center, Tainan, Taiwan.
  • How-Ran Guo
    Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Department of Occupational and Environmental Medicine, National Cheng Kung University Hospital, Tainan, Taiwan. Electronic address: hrguo@mail.ncku.edu.tw.

Keywords

External Resources

Popular Topics
Recent Journals