This paper is in the following e-collection/theme issue:

Digital Health, Telehealth and e-Innovation in Clinical Settings (401) Machine Learning (1465) Clinical Information and Decision Making (1407) Decision Support for Health Professionals (1185) Artificial Intelligence (1371) Respiratory Medicine (83) Tools, Programs and Algorithms (266)

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

Authors of this article:

Syunsuke Yamanaka1 Author Orcid Image ;   Tadahiro Goto2 Author Orcid Image ;   Koji Morikawa3 Author Orcid Image ;   Hiroko Watase4 Author Orcid Image ;   Hiroshi Okamoto5 Author Orcid Image ;   Yusuke Hagiwara6 Author Orcid Image ;   Kohei Hasegawa7 Author Orcid Image
Article Authors Cited by (11) Tweetations (3) Metrics

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, Japan

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

3 Connect Inc, Tokyo, Japan

4 Department of Surgery, University of Washington, Seattle, WA, United States

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

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

7 Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, United States

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, 113-8655
  • Japan
  • Phone: 81 3-5841-1887
  • Email: tag695@mail.harvard.edu