A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy

Transthyretin amyloid cardiomyopathy is a treatable but often unrecognized cause of heart failure. We derived and validated a machine learning model based on medical diagnostic codes that identifies heart failure patients at risk for wild-type transthyretin amyloid cardiomyopathy.

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Autores principales:	Ahsan Huda, Adam Castaño, Anindita Niyogi, Jennifer Schumacher, Michelle Stewart, Marianna Bruno, Mo Hu, Faraz S. Ahmad, Rahul C. Deo, Sanjiv J. Shah
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/9df90401b120477884a567e3e9520657
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spelling	oai:doaj.org-article:9df90401b120477884a567e3e95206572021-12-02T15:36:29ZA machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy10.1038/s41467-021-22876-92041-1723https://doaj.org/article/9df90401b120477884a567e3e95206572021-05-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-22876-9https://doaj.org/toc/2041-1723Transthyretin amyloid cardiomyopathy is a treatable but often unrecognized cause of heart failure. We derived and validated a machine learning model based on medical diagnostic codes that identifies heart failure patients at risk for wild-type transthyretin amyloid cardiomyopathy.Ahsan HudaAdam CastañoAnindita NiyogiJennifer SchumacherMichelle StewartMarianna BrunoMo HuFaraz S. AhmadRahul C. DeoSanjiv J. ShahNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Ahsan Huda Adam Castaño Anindita Niyogi Jennifer Schumacher Michelle Stewart Marianna Bruno Mo Hu Faraz S. Ahmad Rahul C. Deo Sanjiv J. Shah A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy
description	Transthyretin amyloid cardiomyopathy is a treatable but often unrecognized cause of heart failure. We derived and validated a machine learning model based on medical diagnostic codes that identifies heart failure patients at risk for wild-type transthyretin amyloid cardiomyopathy.
format	article
author	Ahsan Huda Adam Castaño Anindita Niyogi Jennifer Schumacher Michelle Stewart Marianna Bruno Mo Hu Faraz S. Ahmad Rahul C. Deo Sanjiv J. Shah
author_facet	Ahsan Huda Adam Castaño Anindita Niyogi Jennifer Schumacher Michelle Stewart Marianna Bruno Mo Hu Faraz S. Ahmad Rahul C. Deo Sanjiv J. Shah
author_sort	Ahsan Huda
title	A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy
title_short	A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy
title_full	A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy
title_fullStr	A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy
title_full_unstemmed	A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy
title_sort	machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9df90401b120477884a567e3e9520657
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A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy

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