Machine learning analysis of left ventricular function to characterize heart failure with preserved ejection fraction

Current diagnosis of heart failure with preserved ejection fraction (HFpEF) is suboptimal. We tested the hypothesis that comprehensive machine learning (ML) of left ventricular function at rest and exercise objectively captures differences between HFpEF and healthy subjects.

Detalhes bibliográficos
Autores: Sanchez Martinez, Sergio, Duchateau, Nicolas, Erdei, Tamas, Kunszt, Gabor, Aakhus, Svend, Degiovanni, Anna, Marino, Paolo, Carluccio, Erberto, Piella Fenoy, Gemma, Fraser, Alan G., Bijnens, Bart
Formato: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2018
País:España
Recursos:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/36968
Acesso em linha:http://hdl.handle.net/10230/36968
http://dx.doi.org/10.1161/CIRCIMAGING.117.007138
Access Level:acceso abierto
Palavra-chave:Echocardiography
Machine learning
Early diagnosis
Heart failure
Diastolic
Ultrasonography
Doppler
Stress
Descrição
Resumo:Current diagnosis of heart failure with preserved ejection fraction (HFpEF) is suboptimal. We tested the hypothesis that comprehensive machine learning (ML) of left ventricular function at rest and exercise objectively captures differences between HFpEF and healthy subjects.

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