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
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 ,
KR
2 Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine , Seoul ,
KR
3 Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine , Seoul ,
KR
4 Division of Cardiology, Department of Internal Medicine, Ulsan University Hospital, University of Ulsan College of Medicine , Ulsan ,
KR
5 Artificial Intelligence Lab, Linewalks, Inc , Seoul ,
KR
6 Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine , Seoul ,
KR
*these authors contributed equally
Corresponding Author:
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Young-Hak Kim, MD, PhD
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Division of Cardiology, Department of Internal Medicine, Asan Medical Center
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University of Ulsan College of Medicine
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88 Olympic-ro 43-gil, Songpa-gu
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Seoul
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KR
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Phone:
82 2-3010-3995
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Email: mdyhkim@amc.seoul.kr