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Computational Decision Support for the COVID-19 Healthcare Coalition

The COVID-19 Healthcare Coalition was established as a private sector-led response to the COVID-19 pandemic. Its purpose was to bring together healthcare organizations, technology firms, nonprofits, academia, and startups to preserve the healthcare delivery system and help protect U.S. populations b...

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Detalles Bibliográficos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IEEE 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9295914/
https://www.ncbi.nlm.nih.gov/pubmed/35916867
http://dx.doi.org/10.1109/MCSE.2020.3036586
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description The COVID-19 Healthcare Coalition was established as a private sector-led response to the COVID-19 pandemic. Its purpose was to bring together healthcare organizations, technology firms, nonprofits, academia, and startups to preserve the healthcare delivery system and help protect U.S. populations by providing data-driven, real-time insights that improve outcomes. This required the coalition to obtain, align, and orchestrate many heterogeneous data sources and present this data on dashboards in a format that was understandable and useful to decision makers. To do this, the coalition employed an ensemble approach to analysis, combining machine learning algorithms together with theory-based simulations, allowing prognosis to provide computational decision support rooted in science and engineering.
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spelling pubmed-92959142022-07-28 Computational Decision Support for the COVID-19 Healthcare Coalition Comput Sci Eng Theme Article: Computational Science in the Fight against Covid-19, Part II The COVID-19 Healthcare Coalition was established as a private sector-led response to the COVID-19 pandemic. Its purpose was to bring together healthcare organizations, technology firms, nonprofits, academia, and startups to preserve the healthcare delivery system and help protect U.S. populations by providing data-driven, real-time insights that improve outcomes. This required the coalition to obtain, align, and orchestrate many heterogeneous data sources and present this data on dashboards in a format that was understandable and useful to decision makers. To do this, the coalition employed an ensemble approach to analysis, combining machine learning algorithms together with theory-based simulations, allowing prognosis to provide computational decision support rooted in science and engineering. IEEE 2020-11-06 /pmc/articles/PMC9295914/ /pubmed/35916867 http://dx.doi.org/10.1109/MCSE.2020.3036586 Text en This article is free to access and download, along with rights for full text and data mining, re-use and analysis.
spellingShingle Theme Article: Computational Science in the Fight against Covid-19, Part II
Computational Decision Support for the COVID-19 Healthcare Coalition
title Computational Decision Support for the COVID-19 Healthcare Coalition
title_full Computational Decision Support for the COVID-19 Healthcare Coalition
title_fullStr Computational Decision Support for the COVID-19 Healthcare Coalition
title_full_unstemmed Computational Decision Support for the COVID-19 Healthcare Coalition
title_short Computational Decision Support for the COVID-19 Healthcare Coalition
title_sort computational decision support for the covid-19 healthcare coalition
topic Theme Article: Computational Science in the Fight against Covid-19, Part II
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9295914/
https://www.ncbi.nlm.nih.gov/pubmed/35916867
http://dx.doi.org/10.1109/MCSE.2020.3036586
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