Translating Complex Machine-Learning Phenogrouping Into Simple Algorithm: Atrium, Ventricle, and Fibrosis in Mitral Valve Prolapse
- PMID: 37676208
- DOI: 10.1016/j.jcmg.2023.07.010
Translating Complex Machine-Learning Phenogrouping Into Simple Algorithm: Atrium, Ventricle, and Fibrosis in Mitral Valve Prolapse
Keywords: cardiac remodeling; machine learning; mitral regurgitation; mitral valve prolapse; unsupervised learning.
Conflict of interest statement
Funding Support and Author Disclosures Dr Kagiyama has received research grants from EchoNous Inc and AMI Inc; and is affiliated with a department funded by Philips Japan, KYOCERA, AMI Inc, Inter Reha Co, and Fukuda Denshi, Ltd.
Comment on
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Machine Learning-Based Phenogrouping in MVP Identifies Profiles Associated With Myocardial Fibrosis and Cardiovascular Events.JACC Cardiovasc Imaging. 2023 Oct;16(10):1271-1284. doi: 10.1016/j.jcmg.2023.03.009. Epub 2023 May 17. JACC Cardiovasc Imaging. 2023. PMID: 37204382
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