About: COVID-19 diagnosis prediction by symptoms of tested individuals: a machine learning approach

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	COVID-19 diagnosis prediction by symptoms of tested individuals: a machine learning approach
Creator	Shomron, Noam Zoabi, Yazeed
Source	MedRxiv
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 in hopes of assisting medical staff worldwide in triaging patients when allocating limited healthcare resources. We established a machine learning approach that trained on records from 51,831 tested individuals (of whom 4,769 were confirmed COVID-19 cases) while the test set contained data from the following week (47,401 tested individuals of whom 3,624 were confirmed COVID-19 cases). Our model predicts COVID-19 test results with high accuracy using only 8 features: gender, whether the age is above 60, information about close contact with an infected individual, and 5 initial clinical symptoms. Overall, using nationwide data representing the general population, we developed a model that enables screening suspected COVID-19 patients according to simple features accessed by asking them basic questions. Our model can be used, among other considerations, to prioritize testing for COVID-19 when allocating limited testing resources.
has issue date	2020-05-11(xsd:dateTime)
bibo:doi	10.1101/2020.05.07.20093948
has license	medrxiv
sha1sum (hex)	c49b814c63258f8bab2be7ff4462cb6fe2601139
schema:url	https://doi.org/10.1101/2020.05.07.20093948
resource representing a document's title	COVID-19 diagnosis prediction by symptoms of tested individuals: a machine learning approach
resource representing a document's body	covid:c49b814c63258f8bab2be7ff4462cb6fe2601139#body_text
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