Deep neural network-estimated electrocardiographic age as a mortality predictor

The electrocardiogram (ECG) is the most commonly used exam for the screening and evaluation of cardiovascular diseases. Here, the authors propose that the age predicted by artificial intelligence from the raw ECG tracing can be a measure of cardiovascular health and provide prognostic information.

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Autores principales:	Emilly M. Lima, Antônio H. Ribeiro, Gabriela M. M. Paixão, Manoel Horta Ribeiro, Marcelo M. Pinto-Filho, Paulo R. Gomes, Derick M. Oliveira, Ester C. Sabino, Bruce B. Duncan, Luana Giatti, Sandhi M. Barreto, Wagner Meira Jr, Thomas B. Schön, Antonio Luiz P. Ribeiro
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/f1364f686bf341b4863accec047f7a93
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spelling	oai:doaj.org-article:f1364f686bf341b4863accec047f7a932021-12-02T15:09:10ZDeep neural network-estimated electrocardiographic age as a mortality predictor10.1038/s41467-021-25351-72041-1723https://doaj.org/article/f1364f686bf341b4863accec047f7a932021-08-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-25351-7https://doaj.org/toc/2041-1723The electrocardiogram (ECG) is the most commonly used exam for the screening and evaluation of cardiovascular diseases. Here, the authors propose that the age predicted by artificial intelligence from the raw ECG tracing can be a measure of cardiovascular health and provide prognostic information.Emilly M. LimaAntônio H. RibeiroGabriela M. M. PaixãoManoel Horta RibeiroMarcelo M. Pinto-FilhoPaulo R. GomesDerick M. OliveiraEster C. SabinoBruce B. DuncanLuana GiattiSandhi M. BarretoWagner Meira JrThomas B. SchönAntonio Luiz P. RibeiroNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Emilly M. Lima Antônio H. Ribeiro Gabriela M. M. Paixão Manoel Horta Ribeiro Marcelo M. Pinto-Filho Paulo R. Gomes Derick M. Oliveira Ester C. Sabino Bruce B. Duncan Luana Giatti Sandhi M. Barreto Wagner Meira Jr Thomas B. Schön Antonio Luiz P. Ribeiro Deep neural network-estimated electrocardiographic age as a mortality predictor
description	The electrocardiogram (ECG) is the most commonly used exam for the screening and evaluation of cardiovascular diseases. Here, the authors propose that the age predicted by artificial intelligence from the raw ECG tracing can be a measure of cardiovascular health and provide prognostic information.
format	article
author	Emilly M. Lima Antônio H. Ribeiro Gabriela M. M. Paixão Manoel Horta Ribeiro Marcelo M. Pinto-Filho Paulo R. Gomes Derick M. Oliveira Ester C. Sabino Bruce B. Duncan Luana Giatti Sandhi M. Barreto Wagner Meira Jr Thomas B. Schön Antonio Luiz P. Ribeiro
author_facet	Emilly M. Lima Antônio H. Ribeiro Gabriela M. M. Paixão Manoel Horta Ribeiro Marcelo M. Pinto-Filho Paulo R. Gomes Derick M. Oliveira Ester C. Sabino Bruce B. Duncan Luana Giatti Sandhi M. Barreto Wagner Meira Jr Thomas B. Schön Antonio Luiz P. Ribeiro
author_sort	Emilly M. Lima
title	Deep neural network-estimated electrocardiographic age as a mortality predictor
title_short	Deep neural network-estimated electrocardiographic age as a mortality predictor
title_full	Deep neural network-estimated electrocardiographic age as a mortality predictor
title_fullStr	Deep neural network-estimated electrocardiographic age as a mortality predictor
title_full_unstemmed	Deep neural network-estimated electrocardiographic age as a mortality predictor
title_sort	deep neural network-estimated electrocardiographic age as a mortality predictor
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f1364f686bf341b4863accec047f7a93
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Deep neural network-estimated electrocardiographic age as a mortality predictor

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