Scalable and accurate deep learning with electronic health records

Artificial intelligence: Algorithm predicts clinical outcomes for hospital inpatients Artificial intelligence outperforms traditional statistical models at predicting a range of clinical outcomes from a patient’s entire raw electronic health record (EHR). A team led by Alvin Rajkomar and Eyal Oren f...

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Autores principales:	Alvin Rajkomar, Eyal Oren, Kai Chen, Andrew M. Dai, Nissan Hajaj, Michaela Hardt, Peter J. Liu, Xiaobing Liu, Jake Marcus, Mimi Sun, Patrik Sundberg, Hector Yee, Kun Zhang, Yi Zhang, Gerardo Flores, Gavin E. Duggan, Jamie Irvine, Quoc Le, Kurt Litsch, Alexander Mossin, Justin Tansuwan, De Wang, James Wexler, Jimbo Wilson, Dana Ludwig, Samuel L. Volchenboum, Katherine Chou, Michael Pearson, Srinivasan Madabushi, Nigam H. Shah, Atul J. Butte, Michael D. Howell, Claire Cui, Greg S. Corrado, Jeffrey Dean
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/a9b3a7cdda6e486cba268a16dc6760b2
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spelling	oai:doaj.org-article:a9b3a7cdda6e486cba268a16dc6760b22021-12-02T14:22:07ZScalable and accurate deep learning with electronic health records10.1038/s41746-018-0029-12398-6352https://doaj.org/article/a9b3a7cdda6e486cba268a16dc6760b22018-05-01T00:00:00Zhttps://doi.org/10.1038/s41746-018-0029-1https://doaj.org/toc/2398-6352Artificial intelligence: Algorithm predicts clinical outcomes for hospital inpatients Artificial intelligence outperforms traditional statistical models at predicting a range of clinical outcomes from a patient’s entire raw electronic health record (EHR). A team led by Alvin Rajkomar and Eyal Oren from Google in Mountain View, California, USA, developed a data processing pipeline for transforming EHR files into a standardized format. They then applied deep learning models to data from 216,221 adult patients hospitalized for at least 24 h each at two academic medical centers, and showed that their algorithm could accurately predict risk of mortality, hospital readmission, prolonged hospital stay and discharge diagnosis. In all cases, the method proved more accurate than previously published models. The authors provide a case study to serve as a proof-of-concept of how such an algorithm could be used in routine clinical practice in the future.Alvin RajkomarEyal OrenKai ChenAndrew M. DaiNissan HajajMichaela HardtPeter J. LiuXiaobing LiuJake MarcusMimi SunPatrik SundbergHector YeeKun ZhangYi ZhangGerardo FloresGavin E. DugganJamie IrvineQuoc LeKurt LitschAlexander MossinJustin TansuwanDe WangJames WexlerJimbo WilsonDana LudwigSamuel L. VolchenboumKatherine ChouMichael PearsonSrinivasan MadabushiNigam H. ShahAtul J. ButteMichael D. HowellClaire CuiGreg S. CorradoJeffrey DeanNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 1, Iss 1, Pp 1-10 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Alvin Rajkomar Eyal Oren Kai Chen Andrew M. Dai Nissan Hajaj Michaela Hardt Peter J. Liu Xiaobing Liu Jake Marcus Mimi Sun Patrik Sundberg Hector Yee Kun Zhang Yi Zhang Gerardo Flores Gavin E. Duggan Jamie Irvine Quoc Le Kurt Litsch Alexander Mossin Justin Tansuwan De Wang James Wexler Jimbo Wilson Dana Ludwig Samuel L. Volchenboum Katherine Chou Michael Pearson Srinivasan Madabushi Nigam H. Shah Atul J. Butte Michael D. Howell Claire Cui Greg S. Corrado Jeffrey Dean Scalable and accurate deep learning with electronic health records
description	Artificial intelligence: Algorithm predicts clinical outcomes for hospital inpatients Artificial intelligence outperforms traditional statistical models at predicting a range of clinical outcomes from a patient’s entire raw electronic health record (EHR). A team led by Alvin Rajkomar and Eyal Oren from Google in Mountain View, California, USA, developed a data processing pipeline for transforming EHR files into a standardized format. They then applied deep learning models to data from 216,221 adult patients hospitalized for at least 24 h each at two academic medical centers, and showed that their algorithm could accurately predict risk of mortality, hospital readmission, prolonged hospital stay and discharge diagnosis. In all cases, the method proved more accurate than previously published models. The authors provide a case study to serve as a proof-of-concept of how such an algorithm could be used in routine clinical practice in the future.
format	article
author	Alvin Rajkomar Eyal Oren Kai Chen Andrew M. Dai Nissan Hajaj Michaela Hardt Peter J. Liu Xiaobing Liu Jake Marcus Mimi Sun Patrik Sundberg Hector Yee Kun Zhang Yi Zhang Gerardo Flores Gavin E. Duggan Jamie Irvine Quoc Le Kurt Litsch Alexander Mossin Justin Tansuwan De Wang James Wexler Jimbo Wilson Dana Ludwig Samuel L. Volchenboum Katherine Chou Michael Pearson Srinivasan Madabushi Nigam H. Shah Atul J. Butte Michael D. Howell Claire Cui Greg S. Corrado Jeffrey Dean
author_facet	Alvin Rajkomar Eyal Oren Kai Chen Andrew M. Dai Nissan Hajaj Michaela Hardt Peter J. Liu Xiaobing Liu Jake Marcus Mimi Sun Patrik Sundberg Hector Yee Kun Zhang Yi Zhang Gerardo Flores Gavin E. Duggan Jamie Irvine Quoc Le Kurt Litsch Alexander Mossin Justin Tansuwan De Wang James Wexler Jimbo Wilson Dana Ludwig Samuel L. Volchenboum Katherine Chou Michael Pearson Srinivasan Madabushi Nigam H. Shah Atul J. Butte Michael D. Howell Claire Cui Greg S. Corrado Jeffrey Dean
author_sort	Alvin Rajkomar
title	Scalable and accurate deep learning with electronic health records
title_short	Scalable and accurate deep learning with electronic health records
title_full	Scalable and accurate deep learning with electronic health records
title_fullStr	Scalable and accurate deep learning with electronic health records
title_full_unstemmed	Scalable and accurate deep learning with electronic health records
title_sort	scalable and accurate deep learning with electronic health records
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/a9b3a7cdda6e486cba268a16dc6760b2
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Scalable and accurate deep learning with electronic health records

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