Published on in Vol 11, No 1 (2022): Jan-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28366, first published .
Machine Learning Approaches for Predicting Difficult Airway and First-Pass Success in the Emergency Department: Multicenter Prospective Observational Study

Machine Learning Approaches for Predicting Difficult Airway and First-Pass Success in the Emergency Department: Multicenter Prospective Observational Study

Machine Learning Approaches for Predicting Difficult Airway and First-Pass Success in the Emergency Department: Multicenter Prospective Observational Study

Syunsuke Yamanaka 1, MD, PhD;  Tadahiro Goto 2, MD, MPH, PhD;  Koji Morikawa 3, PhD;  Hiroko Watase 4, MD, MPH;  Hiroshi Okamoto 5, MD, MPH;  Yusuke Hagiwara 6, MD, MPH;  Kohei Hasegawa 7, MD, MPH

1 Department of Emergency Medicine & General Internal Medicine, The University of Fukui , Fukui , JP

2 Department of Clinical Epidemiology & Health Economics, School of Public Health, The University of Tokyo, Tokyo , JP

3 Connect Inc , Tokyo , JP

4 Department of Surgery, University of Washington , Seattle, WA, US

5 Department of Intensive Care, St. Luke's International Hospital , Tokyo , JP

6 Department of Pediatric Emergency and Critical Care Medicine, Tokyo Metropolitan Children's Medical Center , Tokyo , JP

7 Department of Emergency Medicine, Massachusetts General Hospital , Boston, MA, US

Corresponding Author:

  • Tadahiro Goto, MD, MPH, PhD
  • Department of Clinical Epidemiology & Health Economics
  • School of Public Health
  • The University of Tokyo
  • 7-3-1 Hongo, Bunkyo-ku
  • Tokyo
  • JP
  • Phone: 81 3-5841-1887
  • Email: tag695@mail.harvard.edu