Heart Failure with preserved Ejection Fraction has proven to be a suitable syndrome to
be studied with machine learning approaches, as its complexity is not fully captured in
clinical guidelines.
For this reason, in this work we present a pipeline to characterize patients from Heart
Failure with Preserved Ejection Fraction cohorts. This comprises from the creation of the
databases to the development of a computational platform in Python to process the images
and extract the descriptors. Lastly, ...
Heart Failure with preserved Ejection Fraction has proven to be a suitable syndrome to
be studied with machine learning approaches, as its complexity is not fully captured in
clinical guidelines.
For this reason, in this work we present a pipeline to characterize patients from Heart
Failure with Preserved Ejection Fraction cohorts. This comprises from the creation of the
databases to the development of a computational platform in Python to process the images
and extract the descriptors. Lastly, we implemented machine learning and dimensionality
reduction techniques to explore the data and clustering and kernel regression to obtain
physiological insights on the population.
We validated the Echocardiographic Image Analysis platform with clinical data from two
clinical trials, succeeded at creating meaningful clusters to classify healthy and diseased
patients and obtained an output space from Multiple Kernel Learning which encoded the
principal modes of cardiac dysfunction.
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