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