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

Artificial Intelligence (1371) Decision Support for Health Professionals (1185) Machine Learning (1465) Computer-Aided Diagnosis (128) Clinical Information and Decision Making (1407) Clinical Informatics (1022) Cardiology (159) Advanced Data Analytics in eHealth (133) AI Applications in Biomedical Engineering (8)

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 (2) 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