Published on in Vol 24, No 4 (2022): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29982, first published .
Predicting Sepsis Mortality in a Population-Based National Database: Machine Learning Approach

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

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

James Yeongjun Park   1 , MSc ;   Tzu-Chun Hsu   2 , BSc ;   Jiun-Ruey Hu   3 , MPH, MD ;   Chun-Yuan Chen   4 , MD ;   Wan-Ting Hsu   5, 6 , MSc ;   Matthew Lee   6 , JD, PhD ;   Joshua Ho   7 , MSc ;   Chien-Chang Lee   2, 7 , MD, SCD

1 Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, United States

2 Department of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan

3 Department of Internal Medicine, Yale School of Medicine, New Haven, CT, United States

4 Department of Medicine, National Taiwan University, Taipei, Taiwan

5 Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, United States

6 Medical Wizdom, LLC, Brookline, MA, United States

7 Center of Intelligent Healthcare, National Taiwan University Hospital, Taipei, Taiwan

Corresponding Author:

  • Chien-Chang Lee, MD, SCD
  • Department of Emergency Medicine
  • National Taiwan University Hospital
  • Number 7, Chung-Shan South Road
  • Taipei, 100
  • Taiwan
  • Phone: 886 223123456
  • Email: hit3transparency@gmail.com