Machine learning-based prediction of COVID-19 diagnosis based on symptoms

Abstract Effective screening of SARS-CoV-2 enables quick and efficient diagnosis of COVID-19 and can mitigate the burden on healthcare systems. Prediction models that combine several features to estimate the risk of infection have been developed. These aim to assist medical staff worldwide in triagi...

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Autores principales:	Yazeed Zoabi, Shira Deri-Rozov, Noam Shomron
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/b5de7cb4698a4a8597004eba78480bac
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spelling	oai:doaj.org-article:b5de7cb4698a4a8597004eba78480bac2021-12-02T16:05:59ZMachine learning-based prediction of COVID-19 diagnosis based on symptoms10.1038/s41746-020-00372-62398-6352https://doaj.org/article/b5de7cb4698a4a8597004eba78480bac2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-00372-6https://doaj.org/toc/2398-6352Abstract Effective screening of SARS-CoV-2 enables quick and efficient diagnosis of COVID-19 and can mitigate the burden on healthcare systems. Prediction models that combine several features to estimate the risk of infection have been developed. These aim to assist medical staff worldwide in triaging patients, especially in the context of limited healthcare resources. We established a machine-learning approach that trained on records from 51,831 tested individuals (of whom 4769 were confirmed to have COVID-19). The test set contained data from the subsequent week (47,401 tested individuals of whom 3624 were confirmed to have COVID-19). Our model predicted COVID-19 test results with high accuracy using only eight binary features: sex, age ≥60 years, known contact with an infected individual, and the appearance of five initial clinical symptoms. Overall, based on the nationwide data publicly reported by the Israeli Ministry of Health, we developed a model that detects COVID-19 cases by simple features accessed by asking basic questions. Our framework can be used, among other considerations, to prioritize testing for COVID-19 when testing resources are limited.Yazeed ZoabiShira Deri-RozovNoam ShomronNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-5 (2021)
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 Yazeed Zoabi Shira Deri-Rozov Noam Shomron Machine learning-based prediction of COVID-19 diagnosis based on symptoms
description	Abstract Effective screening of SARS-CoV-2 enables quick and efficient diagnosis of COVID-19 and can mitigate the burden on healthcare systems. Prediction models that combine several features to estimate the risk of infection have been developed. These aim to assist medical staff worldwide in triaging patients, especially in the context of limited healthcare resources. We established a machine-learning approach that trained on records from 51,831 tested individuals (of whom 4769 were confirmed to have COVID-19). The test set contained data from the subsequent week (47,401 tested individuals of whom 3624 were confirmed to have COVID-19). Our model predicted COVID-19 test results with high accuracy using only eight binary features: sex, age ≥60 years, known contact with an infected individual, and the appearance of five initial clinical symptoms. Overall, based on the nationwide data publicly reported by the Israeli Ministry of Health, we developed a model that detects COVID-19 cases by simple features accessed by asking basic questions. Our framework can be used, among other considerations, to prioritize testing for COVID-19 when testing resources are limited.
format	article
author	Yazeed Zoabi Shira Deri-Rozov Noam Shomron
author_facet	Yazeed Zoabi Shira Deri-Rozov Noam Shomron
author_sort	Yazeed Zoabi
title	Machine learning-based prediction of COVID-19 diagnosis based on symptoms
title_short	Machine learning-based prediction of COVID-19 diagnosis based on symptoms
title_full	Machine learning-based prediction of COVID-19 diagnosis based on symptoms
title_fullStr	Machine learning-based prediction of COVID-19 diagnosis based on symptoms
title_full_unstemmed	Machine learning-based prediction of COVID-19 diagnosis based on symptoms
title_sort	machine learning-based prediction of covid-19 diagnosis based on symptoms
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b5de7cb4698a4a8597004eba78480bac
work_keys_str_mv	AT yazeedzoabi machinelearningbasedpredictionofcovid19diagnosisbasedonsymptoms AT shiraderirozov machinelearningbasedpredictionofcovid19diagnosisbasedonsymptoms AT noamshomron machinelearningbasedpredictionofcovid19diagnosisbasedonsymptoms
_version_	1718385171146735616

Machine learning-based prediction of COVID-19 diagnosis based on symptoms

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