Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67576, first published .
Deep Learning–Based Electrocardiogram Model (EIANet) to Predict Emergency Department Cardiac Arrest: Development and External Validation Study

Deep Learning–Based Electrocardiogram Model (EIANet) to Predict Emergency Department Cardiac Arrest: Development and External Validation Study

Deep Learning–Based Electrocardiogram Model (EIANet) to Predict Emergency Department Cardiac Arrest: Development and External Validation Study

Journals

  1. Lialiou P, Maglogiannis I. Students’ Burnout Symptoms Detection Using Smartwatch Wearable Devices: A Systematic Literature Review. AI Sensors 2025;1(1):2 View
  2. Feng N, Chen C, Du P, Gong C, Pei J, Huang D. MS-LTCAF: A Multi-Scale Lead-Temporal Co-Attention Framework for ECG Arrhythmia Detection. Bioengineering 2025;12(9):1007 View
  3. 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
  4. Franceschi F, Ayar P, Hassan T, Gries A. Artificial intelligence to improve patient care in emergency medicine: a workflow-based analysis. Internal and Emergency Medicine 2025 View