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

Machine Learning (1121) Electronic Health Records (839) Decision Support for Health Professionals (853) Clinical Information and Decision Making (1040) Artificial Intelligence (822) Secondary Use of Clinical Data for Research and Surveillance (266)

Published on in Vol 10 , No 5 (2022) :May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26801, first published .
Electronic Medical Record–Based Machine Learning Approach to Predict the Risk of 30-Day Adverse Cardiac Events After Invasive Coronary Treatment: Machine Learning Model Development and Validation

Electronic Medical Record–Based Machine Learning Approach to Predict the Risk of 30-Day Adverse Cardiac Events After Invasive Coronary Treatment: Machine Learning Model Development and Validation

Electronic Medical Record–Based Machine Learning Approach to Predict the Risk of 30-Day Adverse Cardiac Events After Invasive Coronary Treatment: Machine Learning Model Development and Validation

Authors of this article:

Osung Kwon 1 Author Orcid Image ;   Wonjun Na 2 Author Orcid Image ;   Heejun Kang 3 Author Orcid Image ;   Tae Joon Jun 3 Author Orcid Image ;   Jihoon Kweon 3 Author Orcid Image ;   Gyung-Min Park 4 Author Orcid Image ;   YongHyun Cho 5 Author Orcid Image ;   Cinyoung Hur 5 Author Orcid Image ;   Jungwoo Chae 5 Author Orcid Image ;   Do-Yoon Kang 3 Author Orcid Image ;   Pil Hyung Lee 3 Author Orcid Image ;   Jung-Min Ahn 3 Author Orcid Image ;   Duk-Woo Park 3 Author Orcid Image ;   Soo-Jin Kang 3 Author Orcid Image ;   Seung-Whan Lee 3 Author Orcid Image ;   Cheol Whan Lee 3 Author Orcid Image ;   Seong-Wook Park 3 Author Orcid Image ;   Seung-Jung Park 3 Author Orcid Image ;   Dong Hyun Yang 6 Author Orcid Image ;   Young-Hak Kim 3 Author Orcid Image
Article Authors Cited by (1) Tweetations (5) Metrics

Osung Kwon   1 * , MD, PhD ;   Wonjun Na   2 * , MS ;   Heejun Kang   3 , MS ;   Tae Joon Jun   3 , PhD ;   Jihoon Kweon   3 , PhD ;   Gyung-Min Park   4 , MD, PhD ;   YongHyun Cho   5 , MS ;   Cinyoung Hur   5 , MS ;   Jungwoo Chae   5 , BS ;   Do-Yoon Kang   3 , MD, PhD ;   Pil Hyung Lee   3 , MD, PhD ;   Jung-Min Ahn   3 , MD, PhD ;   Duk-Woo Park   3 , MD, PhD ;   Soo-Jin Kang   3 , MD, PhD ;   Seung-Whan Lee   3 , MD, PhD ;   Cheol Whan Lee   3 , MD, PhD ;   Seong-Wook Park   3 , MD, PhD ;   Seung-Jung Park   3 , MD, PhD ;   Dong Hyun Yang   6 * , MD, PhD ;   Young-Hak Kim   3 * , MD, PhD

1 Division of Cardiology Department of Internal Medicine, Eunpyeong St Mary's Hospital, Catholic University of Korea, Seoul, Republic of Korea

2 Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea

3 Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea

4 Division of Cardiology, Department of Internal Medicine, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Republic of Korea

5 Artificial Intelligence Lab, Linewalks, Inc, Seoul, Republic of Korea

6 Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea

*these authors contributed equally

Corresponding Author:

  • Young-Hak Kim, MD, PhD
  • Division of Cardiology, Department of Internal Medicine, Asan Medical Center
  • University of Ulsan College of Medicine
  • 88 Olympic-ro 43-gil, Songpa-gu
  • Seoul, 05505
  • Republic of Korea
  • Phone: 82 2-3010-3995
  • Fax: 82 -2-486-5918
  • Email: mdyhkim@amc.seoul.kr