Cross-site transportability of an explainable artificial intelligence model for acute kidney injury prediction

Artificial intelligence (AI) has demonstrated promise in predicting acutekidney injury (AKI), however, clinical adoption of these models requires interpretability and transportability across sites. Here, the authors develop an AKI prediction model and a measure for model transportability across six...

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Autores principales:	Xing Song, Alan S. L. Yu, John A. Kellum, Lemuel R. Waitman, Michael E. Matheny, Steven Q. Simpson, Yong Hu, Mei Liu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/54f7a10e954145e38407819c041b1309
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spelling	oai:doaj.org-article:54f7a10e954145e38407819c041b13092021-12-02T14:40:42ZCross-site transportability of an explainable artificial intelligence model for acute kidney injury prediction10.1038/s41467-020-19551-w2041-1723https://doaj.org/article/54f7a10e954145e38407819c041b13092020-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-19551-whttps://doaj.org/toc/2041-1723Artificial intelligence (AI) has demonstrated promise in predicting acutekidney injury (AKI), however, clinical adoption of these models requires interpretability and transportability across sites. Here, the authors develop an AKI prediction model and a measure for model transportability across six independent health systems.Xing SongAlan S. L. YuJohn A. KellumLemuel R. WaitmanMichael E. MathenySteven Q. SimpsonYong HuMei LiuNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-12 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Xing Song Alan S. L. Yu John A. Kellum Lemuel R. Waitman Michael E. Matheny Steven Q. Simpson Yong Hu Mei Liu Cross-site transportability of an explainable artificial intelligence model for acute kidney injury prediction
description	Artificial intelligence (AI) has demonstrated promise in predicting acutekidney injury (AKI), however, clinical adoption of these models requires interpretability and transportability across sites. Here, the authors develop an AKI prediction model and a measure for model transportability across six independent health systems.
format	article
author	Xing Song Alan S. L. Yu John A. Kellum Lemuel R. Waitman Michael E. Matheny Steven Q. Simpson Yong Hu Mei Liu
author_facet	Xing Song Alan S. L. Yu John A. Kellum Lemuel R. Waitman Michael E. Matheny Steven Q. Simpson Yong Hu Mei Liu
author_sort	Xing Song
title	Cross-site transportability of an explainable artificial intelligence model for acute kidney injury prediction
title_short	Cross-site transportability of an explainable artificial intelligence model for acute kidney injury prediction
title_full	Cross-site transportability of an explainable artificial intelligence model for acute kidney injury prediction
title_fullStr	Cross-site transportability of an explainable artificial intelligence model for acute kidney injury prediction
title_full_unstemmed	Cross-site transportability of an explainable artificial intelligence model for acute kidney injury prediction
title_sort	cross-site transportability of an explainable artificial intelligence model for acute kidney injury prediction
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/54f7a10e954145e38407819c041b1309
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Cross-site transportability of an explainable artificial intelligence model for acute kidney injury prediction

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