Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram

Diagnosing a heart attack requires excessive testing and prolonged observation, which frequently requires hospital admission. Here the authors report a machine learning-based system based exclusively on ECG data that can help clinicians identify 37% more heart attacks during initial screening.

Guardado en:

Detalles Bibliográficos
Autores principales:	Salah Al-Zaiti, Lucas Besomi, Zeineb Bouzid, Ziad Faramand, Stephanie Frisch, Christian Martin-Gill, Richard Gregg, Samir Saba, Clifton Callaway, Ervin Sejdić
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/142323e4d75448858fff5469968e3125
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:142323e4d75448858fff5469968e3125
record_format	dspace
spelling	oai:doaj.org-article:142323e4d75448858fff5469968e31252021-12-02T17:06:35ZMachine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram10.1038/s41467-020-17804-22041-1723https://doaj.org/article/142323e4d75448858fff5469968e31252020-08-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-17804-2https://doaj.org/toc/2041-1723Diagnosing a heart attack requires excessive testing and prolonged observation, which frequently requires hospital admission. Here the authors report a machine learning-based system based exclusively on ECG data that can help clinicians identify 37% more heart attacks during initial screening.Salah Al-ZaitiLucas BesomiZeineb BouzidZiad FaramandStephanie FrischChristian Martin-GillRichard GreggSamir SabaClifton CallawayErvin SejdićNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Salah Al-Zaiti Lucas Besomi Zeineb Bouzid Ziad Faramand Stephanie Frisch Christian Martin-Gill Richard Gregg Samir Saba Clifton Callaway Ervin Sejdić Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram
description	Diagnosing a heart attack requires excessive testing and prolonged observation, which frequently requires hospital admission. Here the authors report a machine learning-based system based exclusively on ECG data that can help clinicians identify 37% more heart attacks during initial screening.
format	article
author	Salah Al-Zaiti Lucas Besomi Zeineb Bouzid Ziad Faramand Stephanie Frisch Christian Martin-Gill Richard Gregg Samir Saba Clifton Callaway Ervin Sejdić
author_facet	Salah Al-Zaiti Lucas Besomi Zeineb Bouzid Ziad Faramand Stephanie Frisch Christian Martin-Gill Richard Gregg Samir Saba Clifton Callaway Ervin Sejdić
author_sort	Salah Al-Zaiti
title	Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram
title_short	Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram
title_full	Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram
title_fullStr	Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram
title_full_unstemmed	Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram
title_sort	machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/142323e4d75448858fff5469968e3125
work_keys_str_mv	AT salahalzaiti machinelearningbasedpredictionofacutecoronarysyndromeusingonlytheprehospital12leadelectrocardiogram AT lucasbesomi machinelearningbasedpredictionofacutecoronarysyndromeusingonlytheprehospital12leadelectrocardiogram AT zeinebbouzid machinelearningbasedpredictionofacutecoronarysyndromeusingonlytheprehospital12leadelectrocardiogram AT ziadfaramand machinelearningbasedpredictionofacutecoronarysyndromeusingonlytheprehospital12leadelectrocardiogram AT stephaniefrisch machinelearningbasedpredictionofacutecoronarysyndromeusingonlytheprehospital12leadelectrocardiogram AT christianmartingill machinelearningbasedpredictionofacutecoronarysyndromeusingonlytheprehospital12leadelectrocardiogram AT richardgregg machinelearningbasedpredictionofacutecoronarysyndromeusingonlytheprehospital12leadelectrocardiogram AT samirsaba machinelearningbasedpredictionofacutecoronarysyndromeusingonlytheprehospital12leadelectrocardiogram AT cliftoncallaway machinelearningbasedpredictionofacutecoronarysyndromeusingonlytheprehospital12leadelectrocardiogram AT ervinsejdic machinelearningbasedpredictionofacutecoronarysyndromeusingonlytheprehospital12leadelectrocardiogram
_version_	1718381540051779584

Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram

Ejemplares similares