Explainable artificial intelligence model to predict acute critical illness from electronic health records

Acute critical illness is often preceded by deterioration of routinely measured clinical parameters, e.g., blood pressure and heart rate. Here, the authors develop an explainable artificial intelligence early warning score system for its early detection.

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Autores principales:	Simon Meyer Lauritsen, Mads Kristensen, Mathias Vassard Olsen, Morten Skaarup Larsen, Katrine Meyer Lauritsen, Marianne Johansson Jørgensen, Jeppe Lange, Bo Thiesson
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/b6bf21b3f09e4e2ab17325dc1d6b1879
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spelling	oai:doaj.org-article:b6bf21b3f09e4e2ab17325dc1d6b18792021-12-02T16:31:50ZExplainable artificial intelligence model to predict acute critical illness from electronic health records10.1038/s41467-020-17431-x2041-1723https://doaj.org/article/b6bf21b3f09e4e2ab17325dc1d6b18792020-07-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-17431-xhttps://doaj.org/toc/2041-1723Acute critical illness is often preceded by deterioration of routinely measured clinical parameters, e.g., blood pressure and heart rate. Here, the authors develop an explainable artificial intelligence early warning score system for its early detection.Simon Meyer LauritsenMads KristensenMathias Vassard OlsenMorten Skaarup LarsenKatrine Meyer LauritsenMarianne Johansson JørgensenJeppe LangeBo ThiessonNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Simon Meyer Lauritsen Mads Kristensen Mathias Vassard Olsen Morten Skaarup Larsen Katrine Meyer Lauritsen Marianne Johansson Jørgensen Jeppe Lange Bo Thiesson Explainable artificial intelligence model to predict acute critical illness from electronic health records
description	Acute critical illness is often preceded by deterioration of routinely measured clinical parameters, e.g., blood pressure and heart rate. Here, the authors develop an explainable artificial intelligence early warning score system for its early detection.
format	article
author	Simon Meyer Lauritsen Mads Kristensen Mathias Vassard Olsen Morten Skaarup Larsen Katrine Meyer Lauritsen Marianne Johansson Jørgensen Jeppe Lange Bo Thiesson
author_facet	Simon Meyer Lauritsen Mads Kristensen Mathias Vassard Olsen Morten Skaarup Larsen Katrine Meyer Lauritsen Marianne Johansson Jørgensen Jeppe Lange Bo Thiesson
author_sort	Simon Meyer Lauritsen
title	Explainable artificial intelligence model to predict acute critical illness from electronic health records
title_short	Explainable artificial intelligence model to predict acute critical illness from electronic health records
title_full	Explainable artificial intelligence model to predict acute critical illness from electronic health records
title_fullStr	Explainable artificial intelligence model to predict acute critical illness from electronic health records
title_full_unstemmed	Explainable artificial intelligence model to predict acute critical illness from electronic health records
title_sort	explainable artificial intelligence model to predict acute critical illness from electronic health records
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/b6bf21b3f09e4e2ab17325dc1d6b1879
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Explainable artificial intelligence model to predict acute critical illness from electronic health records

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