Predicting Sepsis Mortality in a Population-Based National Database: Machine Learning Approach.

Journal: Journal of medical Internet research
Published Date: Apr 13, 2022

Abstract

BACKGROUND: Although machine learning (ML) algorithms have been applied to point-of-care sepsis prognostication, ML has not been used to predict sepsis mortality in an administrative database. Therefore, we examined the performance of common ML algorithms in predicting sepsis mortality in adult patients with sepsis and compared it with that of the conventional context knowledge-based logistic regression approach.

Authors

  • James Yeongjun Park
    Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, United States.
  • Tzu-Chun Hsu
    Department of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan.
  • Jiun-Ruey Hu
    Department of Internal Medicine, Yale School of Medicine, New Haven, CT, United States.
  • Chun-Yuan Chen
    Department of Medicine, National Taiwan University, Taipei, Taiwan.
  • Wan-Ting Hsu
    Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, United States.
  • Matthew Lee
    Department of Urology, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA.
  • Joshua Ho
    Center of Intelligent Healthcare, National Taiwan University Hospital, Taipei, Taiwan.
  • Chien-Chang Lee
    National Taiwan University Hospital, Department of Emergency Medicine, Taipei, Taiwan.

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