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

Coronary Heart Disease (72) Clinical Information and Decision Making (1328) Machine Learning (1383) Cardiac Risk and Cardiac Risk Calculators (32)

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 (3) Tweetations (4) Metrics

Mohamed Bennasar   1 , BSc, MSc, PhD ;   Duncan Banks   2 , BSc, PhD ;   Blaine A Price   1 , MSc ;   Attila Kardos   3 , MD, PhD

1 School of Computing and Comms, The Open University, Milton Keynes, United Kingdom

2 School of Life, Health and Chemical Sciences, The Open University, Milton Keynes, United Kingdom

3 Department of Cardiology, Milton Keynes University Hospital NHS Foundation Trust, Milton Keynes, United Kingdom

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