Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy-Reference-Cited by-同舟云学术

Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy

Published:2018-10-17 Issue:1 Volume:21 Page:74-85
ISSN:1388-9842
Container-title:European Journal of Heart Failure
language:en
Short-container-title:Eur J Heart Fail

Author:

Cikes Maja¹,Sanchez-Martinez Sergio²,Claggett Brian³,Duchateau Nicolas⁴,Piella Gemma²,Butakoff Constantine²,Pouleur Anne Catherine⁵,Knappe Dorit⁶,Biering-Sørensen Tor³⁷,Kutyifa Valentina⁸,Moss Arthur⁸,Stein Kenneth⁹,Solomon Scott D.³,Bijnens Bart²¹⁰

Affiliation:

1. Department of Cardiovascular Diseases; University of Zagreb School of Medicine, and University Hospital Center Zagreb; Zagreb Croatia

2. Department of Information and Communication Technologies; University Pompeu Fabra; Barcelona Spain

3. Brigham and Women's Hospital; Boston MA USA

4. Creatis; CNRS UMR5220, INSERM U1206, Université Lyon 1; France

5. Division of Cardiology; Cliniques Saint-Luc UCL; Brussels Belgium

6. University Heart Center Hamburg; Hamburg Germany

7. Herlev & Gentofte Hospital - Copenhagen University; Copenhagen Denmark

8. University of Rochester; Rochester NY USA

9. Boston Scientific; Minneapolis MN USA

10. ICREA; Barcelona Spain

Funder

‘la Caixa’ Banking Foundation

Fundació la Marató de TV3

‘Programme Avenir Lyon Saint-Etienne’

Boston Scientific

Spanish Ministry of Economy and Competitiveness

FEDER

Publisher

Wiley

Subject

Cardiology and Cardiovascular Medicine

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1002/ejhf.1333/fullpdf

Reference30 articles.

1. Machine learning in heart failure: ready for prime time;Awan;Curr Opin Cardiol,2018

2. Characterization of myocardial motion patterns by unsupervised multiple kernel learning;Sanchez-Martinez;Med Image Anal,2017

3. Phenomapping for novel classification of heart failure with preserved ejection fraction;Shah;Circulation,2015

4. Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis;Motwani;Eur Heart J,2017

5. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs;Gulshan;JAMA,2016

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