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

Machine Learning (2078) Electronic Health Records (1320) Decision Support for Health Professionals (1543) Clinical Information and Decision Making (1919) Artificial Intelligence (2136) Secondary Use of Clinical Data for Research and Surveillance (474)

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 Kwon1 Author Orcid Image ;   Wonjun Na2 Author Orcid Image ;   Heejun Kang3 Author Orcid Image ;   Tae Joon Jun3 Author Orcid Image ;   Jihoon Kweon3 Author Orcid Image ;   Gyung-Min Park4 Author Orcid Image ;   YongHyun Cho5 Author Orcid Image ;   Cinyoung Hur5 Author Orcid Image ;   Jungwoo Chae5 Author Orcid Image ;   Do-Yoon Kang3 Author Orcid Image ;   Pil Hyung Lee3 Author Orcid Image ;   Jung-Min Ahn3 Author Orcid Image ;   Duk-Woo Park3 Author Orcid Image ;   Soo-Jin Kang3 Author Orcid Image ;   Seung-Whan Lee3 Author Orcid Image ;   Cheol Whan Lee3 Author Orcid Image ;   Seong-Wook Park3 Author Orcid Image ;   Seung-Jung Park3 Author Orcid Image ;   Dong Hyun Yang6 Author Orcid Image ;   Young-Hak Kim3 Author Orcid Image
Article Authors Cited by (7) Tweetations (5) Metrics

Journals

  1. Nurmambetova E, Pan J, Zhang Z, Wu G, Lee S, Southern D, Martin E, Ho C, Xu Y, Eastwood C. Developing an Inpatient Electronic Medical Record Phenotype for Hospital-Acquired Pressure Injuries: Case Study Using Natural Language Processing Models. JMIR AI 2023;2:e41264 View
  2. Birlik A, Tozan H, Köse K, Lu H. Machine learning-based hybrid risk estimation system (ERES) in cardiac surgery: Supplementary insights from the ASA score analysis. PLOS Digital Health 2025;4(6):e0000889 View
  3. Jin X, Wang Y, Wang J, Gao Q, Huang Y, Shao L, Zhao J, Li J, Li L, Zhang Z, Li S, Liu Y. A Machine Learning Approach to Differentiate Cold and Hot Syndrome in Viral Pneumonia Integrating Traditional Chinese Medicine and Modern Medicine: Machine Learning Model Development and Validation. JMIR Medical Informatics 2025;13:e64725 View
  4. Kim M, Kim Y, Kang H, Seo H, Choi H, Han J, Kee G, Ko S, Jung H, Kim B, Choi B, Jun T, Kim Y. Leveraging BERT for embedding ICD codes from large scale cardiovascular EMR data to understand patient diagnostic patterns. BMC Medical Informatics and Decision Making 2025;25(1) View
  5. Gao L, Wang G, Yang X, Ge Y, Tong S, Huang W. Online machine learning model for predicting delirium risk in elderly patients with chronic kidney disease: development and preliminary validation. European Journal of Medical Research 2025;30(1) View

Books/Policy Documents

  1. Rodrigues J, Sikkander A, Tripathi S, Kumar K, Mishra S, Theivanathan G. Computational Intelligence for Genomics Data. View

Conference Proceedings

  1. Wahid M, Tafreshi R, Al-Hijji M, Khalil C. 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). A Machine Learning Approach to Predictive Modeling of Cardiovascular Events View