Real-time prediction of COVID-19 related mortality using electronic health records

Identifying COVID-19 patients with the highest mortality risk early is critical to enable effective intervention and optimal prioritisation of care. Here, the authors present a clinical risk scoring system trained on a large data set of patients from 69 healthcare institutions in multiple countries.

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Autores principales:	Patrick Schwab, Arash Mehrjou, Sonali Parbhoo, Leo Anthony Celi, Jürgen Hetzel, Markus Hofer, Bernhard Schölkopf, Stefan Bauer
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/1d4642baba4a44f0810c6f3b7cb930f9
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spelling	oai:doaj.org-article:1d4642baba4a44f0810c6f3b7cb930f92021-12-02T14:21:20ZReal-time prediction of COVID-19 related mortality using electronic health records10.1038/s41467-020-20816-72041-1723https://doaj.org/article/1d4642baba4a44f0810c6f3b7cb930f92021-02-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-20816-7https://doaj.org/toc/2041-1723Identifying COVID-19 patients with the highest mortality risk early is critical to enable effective intervention and optimal prioritisation of care. Here, the authors present a clinical risk scoring system trained on a large data set of patients from 69 healthcare institutions in multiple countries.Patrick SchwabArash MehrjouSonali ParbhooLeo Anthony CeliJürgen HetzelMarkus HoferBernhard SchölkopfStefan BauerNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Patrick Schwab Arash Mehrjou Sonali Parbhoo Leo Anthony Celi Jürgen Hetzel Markus Hofer Bernhard Schölkopf Stefan Bauer Real-time prediction of COVID-19 related mortality using electronic health records
description	Identifying COVID-19 patients with the highest mortality risk early is critical to enable effective intervention and optimal prioritisation of care. Here, the authors present a clinical risk scoring system trained on a large data set of patients from 69 healthcare institutions in multiple countries.
format	article
author	Patrick Schwab Arash Mehrjou Sonali Parbhoo Leo Anthony Celi Jürgen Hetzel Markus Hofer Bernhard Schölkopf Stefan Bauer
author_facet	Patrick Schwab Arash Mehrjou Sonali Parbhoo Leo Anthony Celi Jürgen Hetzel Markus Hofer Bernhard Schölkopf Stefan Bauer
author_sort	Patrick Schwab
title	Real-time prediction of COVID-19 related mortality using electronic health records
title_short	Real-time prediction of COVID-19 related mortality using electronic health records
title_full	Real-time prediction of COVID-19 related mortality using electronic health records
title_fullStr	Real-time prediction of COVID-19 related mortality using electronic health records
title_full_unstemmed	Real-time prediction of COVID-19 related mortality using electronic health records
title_sort	real-time prediction of covid-19 related mortality using electronic health records
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1d4642baba4a44f0810c6f3b7cb930f9
work_keys_str_mv	AT patrickschwab realtimepredictionofcovid19relatedmortalityusingelectronichealthrecords AT arashmehrjou realtimepredictionofcovid19relatedmortalityusingelectronichealthrecords AT sonaliparbhoo realtimepredictionofcovid19relatedmortalityusingelectronichealthrecords AT leoanthonyceli realtimepredictionofcovid19relatedmortalityusingelectronichealthrecords AT jurgenhetzel realtimepredictionofcovid19relatedmortalityusingelectronichealthrecords AT markushofer realtimepredictionofcovid19relatedmortalityusingelectronichealthrecords AT bernhardscholkopf realtimepredictionofcovid19relatedmortalityusingelectronichealthrecords AT stefanbauer realtimepredictionofcovid19relatedmortalityusingelectronichealthrecords
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Real-time prediction of COVID-19 related mortality using electronic health records

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