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

Artificial Intelligence (2129) Decision Support for Health Professionals (1539) Machine Learning (2076) Computer-Aided Diagnosis (251) Clinical Information and Decision Making (1915) Clinical Informatics (1325) Cardiology (224) Advanced Data Analytics in eHealth (209) AI Applications in Biomedical Engineering (15)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/52139, first published .
Artificial Intelligence–Based Electrocardiographic Biomarker for Outcome Prediction in Patients With Acute Heart Failure: Prospective Cohort Study

Artificial Intelligence–Based Electrocardiographic Biomarker for Outcome Prediction in Patients With Acute Heart Failure: Prospective Cohort Study

Artificial Intelligence–Based Electrocardiographic Biomarker for Outcome Prediction in Patients With Acute Heart Failure: Prospective Cohort Study

Authors of this article:

Youngjin Cho1, 2 Author Orcid Image ;   Minjae Yoon1 Author Orcid Image ;   Joonghee Kim2, 3 Author Orcid Image ;   Ji Hyun Lee1 Author Orcid Image ;   Il-Young Oh1 Author Orcid Image ;   Chan Joo Lee4 Author Orcid Image ;   Seok-Min Kang4 Author Orcid Image ;   Dong-Ju Choi1 Author Orcid Image
Article Authors Cited by (11) Tweetations (1) Metrics

Journals

  1. Cho Y, Kim J. Could ChatGPT become a future cardiologist? Navigating requirements and risks. Journal of Cardiovascular Medicine 2024;25(11):772 View
  2. Choi H, Kim J, Park J, Park J, Kim H, Choi H, Yoon Y, Cho G, Cho Y, Hwang I. AI derived ECG global longitudinal strain compared to echocardiographic measurements. Scientific Reports 2024;14(1) View
  3. Kim J, Jung J, Kim J, Cho Y, Lee E, Son D. Non-Inferiority Analysis of Electrocardiography Analysis Application vs. Point-of-Care Ultrasound for Screening Left Ventricular Dysfunction. Yonsei Medical Journal 2025;66(3):172 View
  4. Yoon M, You S. Implementing ECG-AI in Clinical Care. Journal of the American College of Cardiology 2025;85(12):1314 View
  5. Li Y, Xu J, Liu X, Wang X, Zhao C, He K. Development and validation of an integrated prognostic model for all-cause mortality in heart failure: a comprehensive analysis combining clinical, electrocardiographic, and echocardiographic parameters. BMC Cardiovascular Disorders 2025;25(1) View
  6. Meng L, Lian K, Zhang J, Li L, Hu Z. Evolution of Research on Artificial Intelligence for Heart Failure: A Bibliometric and Visual Analysis. Journal of Multidisciplinary Healthcare 2025;Volume 18:2941 View
  7. S V, Jose S, Jirawattanapanit A. Integrating machine learning techniques for enhanced prognostic modeling of heart failure risk in the american population. Physica Scripta 2025;100(7):076011 View
  8. Bekbossynova M, Saliev T, Mukarov M, Sugralimova M, Batpen A, Kozhakhmetova A, Zhanbolat A. Indirect Myocardial Injury in Polytrauma: Mechanistic Pathways and the Clinical Utility of Immunological Markers. Journal of Cardiovascular Development and Disease 2025;12(7):268 View
  9. Lee H, Kwon W, Song K, Jo Y, Kim J, Cho Y, Hwang J, Choi Y. Interethnic validation of electrocardiogram image analysis software for detecting left ventricular dysfunction in an emergency department population. Clinical and Experimental Emergency Medicine 2025;12(3):235 View
  10. Han C, Soh S, Park J, Pak H, Yoon D. Artificial Intelligence–Based Electrocardiogram Model as a Predictor of Postoperative Atrial Fibrillation Following Cardiac Surgery: Retrospective Cohort Study. Journal of Medical Internet Research 2025;27:e77164 View
  11. Niazai A, Jamil H, Hameed M, Sheikh S, Nisar M. Artificial intelligence in cardiovascular diagnostics: a systematic review and descriptive analysis of clinical applications and diagnostic performance. BMC Cardiovascular Disorders 2025;25(1) View