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

Coronary Heart Disease (109) Clinical Information and Decision Making (1915) Machine Learning (2076) Cardiac Risk and Cardiac Risk Calculators (102)

Published on in Vol 4, No 1 (2020): Jan-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/16975, first published .
Minimal Patient Clinical Variables to Accurately Predict Stress Echocardiography Outcome: Validation Study Using Machine Learning Techniques

Minimal Patient Clinical Variables to Accurately Predict Stress Echocardiography Outcome: Validation Study Using Machine Learning Techniques

Minimal Patient Clinical Variables to Accurately Predict Stress Echocardiography Outcome: Validation Study Using Machine Learning Techniques

Authors of this article:

Mohamed Bennasar1 Author Orcid Image ;   Duncan Banks2 Author Orcid Image ;   Blaine A Price1 Author Orcid Image ;   Attila Kardos3 Author Orcid Image
Article Authors Cited by (4) Tweetations (2) Metrics

Journals

  1. de Siqueira V, Borges M, Furtado R, Dourado C, da Costa R. Artificial intelligence applied to support medical decisions for the automatic analysis of echocardiogram images: A systematic review. Artificial Intelligence in Medicine 2021;120:102165 View
  2. Lu J, Stewart J, Bennamoun M, Goudie A, Eshraghian J, Ihdayhid A, Sanfilippo F, Small G, Chow B, Dwivedi G. Deep learning model to predict exercise stress test results: Optimizing the diagnostic test selection strategy and reduce wastage in suspected coronary artery disease patients. Computer Methods and Programs in Biomedicine 2023;240:107717 View
  3. Nagatkina O, Sokolova E, Suvorova O, Antonov D. Technological evolution of inhalers: intelligent systems and controlled delivery to the respiratory tract. Russian Journal of Preventive Medicine 2025;28(8):115 View

Books/Policy Documents

  1. Alkhodari M, Moussa M, Dhou S. Artificial Intelligence and Image Processing in Medical Imaging. View