Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study

Abstract The COVID-19 pandemic has created an urgent need for robust, scalable monitoring tools supporting stratification of high-risk patients. This research aims to develop and validate prediction models, using the UK Biobank, to estimate COVID-19 mortality risk in confirmed cases. From the 11,245...

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Autores principales:	Mohammad A. Dabbah, Angus B. Reed, Adam T. C. Booth, Arrash Yassaee, Aleksa Despotovic, Benjamin Klasmer, Emily Binning, Mert Aral, David Plans, Davide Morelli, Alain B. Labrique, Diwakar Mohan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9352a4ce63234c028f61bb338256452d
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spelling	oai:doaj.org-article:9352a4ce63234c028f61bb338256452d2021-12-02T18:51:52ZMachine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study10.1038/s41598-021-95136-x2045-2322https://doaj.org/article/9352a4ce63234c028f61bb338256452d2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95136-xhttps://doaj.org/toc/2045-2322Abstract The COVID-19 pandemic has created an urgent need for robust, scalable monitoring tools supporting stratification of high-risk patients. This research aims to develop and validate prediction models, using the UK Biobank, to estimate COVID-19 mortality risk in confirmed cases. From the 11,245 participants testing positive for COVID-19, we develop a data-driven random forest classification model with excellent performance (AUC: 0.91), using baseline characteristics, pre-existing conditions, symptoms, and vital signs, such that the score could dynamically assess mortality risk with disease deterioration. We also identify several significant novel predictors of COVID-19 mortality with equivalent or greater predictive value than established high-risk comorbidities, such as detailed anthropometrics and prior acute kidney failure, urinary tract infection, and pneumonias. The model design and feature selection enables utility in outpatient settings. Possible applications include supporting individual-level risk profiling and monitoring disease progression across patients with COVID-19 at-scale, especially in hospital-at-home settings.Mohammad A. DabbahAngus B. ReedAdam T. C. BoothArrash YassaeeAleksa DespotovicBenjamin KlasmerEmily BinningMert AralDavid PlansDavide MorelliAlain B. LabriqueDiwakar MohanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mohammad A. Dabbah Angus B. Reed Adam T. C. Booth Arrash Yassaee Aleksa Despotovic Benjamin Klasmer Emily Binning Mert Aral David Plans Davide Morelli Alain B. Labrique Diwakar Mohan Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study
description	Abstract The COVID-19 pandemic has created an urgent need for robust, scalable monitoring tools supporting stratification of high-risk patients. This research aims to develop and validate prediction models, using the UK Biobank, to estimate COVID-19 mortality risk in confirmed cases. From the 11,245 participants testing positive for COVID-19, we develop a data-driven random forest classification model with excellent performance (AUC: 0.91), using baseline characteristics, pre-existing conditions, symptoms, and vital signs, such that the score could dynamically assess mortality risk with disease deterioration. We also identify several significant novel predictors of COVID-19 mortality with equivalent or greater predictive value than established high-risk comorbidities, such as detailed anthropometrics and prior acute kidney failure, urinary tract infection, and pneumonias. The model design and feature selection enables utility in outpatient settings. Possible applications include supporting individual-level risk profiling and monitoring disease progression across patients with COVID-19 at-scale, especially in hospital-at-home settings.
format	article
author	Mohammad A. Dabbah Angus B. Reed Adam T. C. Booth Arrash Yassaee Aleksa Despotovic Benjamin Klasmer Emily Binning Mert Aral David Plans Davide Morelli Alain B. Labrique Diwakar Mohan
author_facet	Mohammad A. Dabbah Angus B. Reed Adam T. C. Booth Arrash Yassaee Aleksa Despotovic Benjamin Klasmer Emily Binning Mert Aral David Plans Davide Morelli Alain B. Labrique Diwakar Mohan
author_sort	Mohammad A. Dabbah
title	Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study
title_short	Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study
title_full	Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study
title_fullStr	Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study
title_full_unstemmed	Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study
title_sort	machine learning approach to dynamic risk modeling of mortality in covid-19: a uk biobank study
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9352a4ce63234c028f61bb338256452d
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Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study

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