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

Clinical Information and Decision Making (1407) Machine Learning (1465) Artificial Intelligence (1371) Intensive Care Unit (ICU) (112) Decision Support for Health Professionals (1185) Safety and Error Prevention in Health (202)

Published on in Vol 23, No 8 (2021): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23508, first published .
Development and Validation of Unplanned Extubation Prediction Models Using Intensive Care Unit Data: Retrospective, Comparative, Machine Learning Study

Development and Validation of Unplanned Extubation Prediction Models Using Intensive Care Unit Data: Retrospective, Comparative, Machine Learning Study

Development and Validation of Unplanned Extubation Prediction Models Using Intensive Care Unit Data: Retrospective, Comparative, Machine Learning Study

Authors of this article:

Sujeong Hur1, 2 Author Orcid Image ;   Ji Young Min1 Author Orcid Image ;   Junsang Yoo3 Author Orcid Image ;   Kyunga Kim1, 4 Author Orcid Image ;   Chi Ryang Chung5, 6 Author Orcid Image ;   Patricia C Dykes7 Author Orcid Image ;   Won Chul Cha1, 8, 9 Author Orcid Image
Article Authors Cited by (12) Tweetations (6) Metrics

Sujeong Hur   1, 2 * , RN, MS ;   Ji Young Min   1 * , MS ;   Junsang Yoo   3 , RN, PhD ;   Kyunga Kim   1, 4 , PhD ;   Chi Ryang Chung   5, 6 , MD, PhD ;   Patricia C Dykes   7 , RN, PhD ;   Won Chul Cha   1, 8, 9 , MD

1 Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea

2 Department of Patient Experience Management, Samsung Medical Center, Seoul, Republic of Korea

3 Department of Nursing, College of Nursing, Sahmyook University, Seoul, Republic of Korea

4 Biomedical Statistics Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea

5 Department of Critical Care Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

6 Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

7 Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States

8 Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

9 Digital Innovation Center, Samsung Medical Center, Seoul, Republic of Korea

*these authors contributed equally

Corresponding Author:

  • Won Chul Cha, MD
  • Department of Emergency Medicine
  • Samsung Medical Center
  • Sungkyunkwan University School of Medicine
  • 81 Irwon-ro, Gangnam-gu
  • Seoul, 06351
  • Republic of Korea
  • Phone: 82 2-3410-2053
  • Fax: 82 2-3410-0012
  • Email: wc.cha@samsung.com