Early outcome detection for COVID-19 patients

Abstract With the outbreak of COVID-19 exerting a strong pressure on hospitals and health facilities, clinical decision support systems based on predictive models can help to effectively improve the management of the pandemic. We present a method for predicting mortality for COVID-19 patients. Start...

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Autores principales:	Alina Sîrbu, Greta Barbieri, Francesco Faita, Paolo Ferragina, Luna Gargani, Lorenzo Ghiadoni, Corrado Priami
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/34716b8d4543467a9ae9c449c07676a4
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id	oai:doaj.org-article:34716b8d4543467a9ae9c449c07676a4
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spelling	oai:doaj.org-article:34716b8d4543467a9ae9c449c07676a42021-12-02T18:50:48ZEarly outcome detection for COVID-19 patients10.1038/s41598-021-97990-12045-2322https://doaj.org/article/34716b8d4543467a9ae9c449c07676a42021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97990-1https://doaj.org/toc/2045-2322Abstract With the outbreak of COVID-19 exerting a strong pressure on hospitals and health facilities, clinical decision support systems based on predictive models can help to effectively improve the management of the pandemic. We present a method for predicting mortality for COVID-19 patients. Starting from a large number of clinical variables, we select six of them with largest predictive power, using a feature selection method based on genetic algorithms and starting from a set of COVID-19 patients from the first wave. The algorithm is designed to reduce the impact of missing values in the set of variables measured, and consider only variables that show good accuracy on validation data. The final predictive model provides accuracy larger than 85% on test data, including a new patient cohort from the second COVID-19 wave, and on patients with imputed missing values. The selected clinical variables are confirmed to be relevant by recent literature on COVID-19.Alina SîrbuGreta BarbieriFrancesco FaitaPaolo FerraginaLuna GarganiLorenzo GhiadoniCorrado PriamiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Alina Sîrbu Greta Barbieri Francesco Faita Paolo Ferragina Luna Gargani Lorenzo Ghiadoni Corrado Priami Early outcome detection for COVID-19 patients
description	Abstract With the outbreak of COVID-19 exerting a strong pressure on hospitals and health facilities, clinical decision support systems based on predictive models can help to effectively improve the management of the pandemic. We present a method for predicting mortality for COVID-19 patients. Starting from a large number of clinical variables, we select six of them with largest predictive power, using a feature selection method based on genetic algorithms and starting from a set of COVID-19 patients from the first wave. The algorithm is designed to reduce the impact of missing values in the set of variables measured, and consider only variables that show good accuracy on validation data. The final predictive model provides accuracy larger than 85% on test data, including a new patient cohort from the second COVID-19 wave, and on patients with imputed missing values. The selected clinical variables are confirmed to be relevant by recent literature on COVID-19.
format	article
author	Alina Sîrbu Greta Barbieri Francesco Faita Paolo Ferragina Luna Gargani Lorenzo Ghiadoni Corrado Priami
author_facet	Alina Sîrbu Greta Barbieri Francesco Faita Paolo Ferragina Luna Gargani Lorenzo Ghiadoni Corrado Priami
author_sort	Alina Sîrbu
title	Early outcome detection for COVID-19 patients
title_short	Early outcome detection for COVID-19 patients
title_full	Early outcome detection for COVID-19 patients
title_fullStr	Early outcome detection for COVID-19 patients
title_full_unstemmed	Early outcome detection for COVID-19 patients
title_sort	early outcome detection for covid-19 patients
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/34716b8d4543467a9ae9c449c07676a4
work_keys_str_mv	AT alinasirbu earlyoutcomedetectionforcovid19patients AT gretabarbieri earlyoutcomedetectionforcovid19patients AT francescofaita earlyoutcomedetectionforcovid19patients AT paoloferragina earlyoutcomedetectionforcovid19patients AT lunagargani earlyoutcomedetectionforcovid19patients AT lorenzoghiadoni earlyoutcomedetectionforcovid19patients AT corradopriami earlyoutcomedetectionforcovid19patients
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Early outcome detection for COVID-19 patients

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