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

We tested the hypothesis that a machine learning (ML) algorithm utilizing both complex echocardiographic data and clinical parameters could be used to phenogroup a heart failure (HF) cohort and identify patients with beneficial response to cardiac resynchronization therapy (CRT).

Detalles Bibliográficos
Autores: Cikes, Maja, Sanchez Martinez, Sergio, Claggett, Brian, Duchateau, Nicolas, Piella Fenoy, Gemma, Butakoff, Constantine, Pouleur, Anne Catherine, Knappe, Dorit, Biering‐Sørensen, Tor, Kutyifa, Valentina, Moss, Arthur, Stein, Kenneth, Solomon, Scott D., Bijnens, Bart
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2019
País:España
Institución:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/36970
Acceso en línea:http://hdl.handle.net/10230/36970
http://dx.doi.org/10.1002/ejhf.1333
Access Level:acceso abierto
Palabra clave:Machine learning
Heart failure
Personalized medicine
Echocardiography
Cardiac resynchronization therapy
Descripción
Sumario:We tested the hypothesis that a machine learning (ML) algorithm utilizing both complex echocardiographic data and clinical parameters could be used to phenogroup a heart failure (HF) cohort and identify patients with beneficial response to cardiac resynchronization therapy (CRT).

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