Identifying novel phenotypes of elevated left ventricular end diastolic pressure using hierarchical clustering of features derived from electromechanical waveform data

Identifying novel phenotypes of elevated left ventricular end diastolic pressure using hierarchical clustering of features derived from electromechanical waveform data

IntroductionElevated left ventricular end diastolic pressure (LVEDP) is a consequence of compromised left ventricular compliance and an important measure of myocardial dysfunction. An algorithm was developed to predict elevated LVEDP utilizing electro-mechanical (EM) waveform features. We examined t...

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Bibliographic Details
Main Authors: Timothy Burton, Shyam Ramchandani, Sanjeev P. Bhavnani, Rola Khedraki, Travis J. Cohoon, Thomas D. Stuckey, John A. Steuter, Frederick J. Meine, Brett A. Bennett, William S. Carroll, Emmanuel Lange, Farhad Fathieh, Ali Khosousi, Mark Rabbat, William E. Sanders
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-09-01
Series:Frontiers in Cardiovascular Medicine
Subjects:
machine learning
risk stratification
left ventricular filling pressures
artificial intelligence
digital health
Online Access:https://www.frontiersin.org/articles/10.3389/fcvm.2022.980625/full

Internet

https://www.frontiersin.org/articles/10.3389/fcvm.2022.980625/full

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