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Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>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 \u226560 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.<\/jats:p>","DOI":"10.1038\/s41746-020-00372-6","type":"journal-article","created":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T06:02:41Z","timestamp":1609740161000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":419,"title":["Machine learning-based prediction of COVID-19 diagnosis based on symptoms"],"prefix":"10.1038","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5501-0036","authenticated-orcid":false,"given":"Yazeed","family":"Zoabi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shira","family":"Deri-Rozov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9913-6124","authenticated-orcid":false,"given":"Noam","family":"Shomron","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,1,4]]},"reference":[{"key":"372_CR1","doi-asserted-by":"publisher","unstructured":"Dong, E., Du, H. & Gardner, L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect. Dis. https:\/\/doi.org\/10.1016\/S1473-3099(20)30120-1 (2020).","DOI":"10.1016\/S1473-3099(20)30120-1"},{"key":"372_CR2","unstructured":"Gozes, O. et al. Rapid AI development cycle for the coronavirus (COVID-19) pandemic: initial results for automated detection & patient monitoring using deep learning CT image analysis. arXiv e-prints 2003, arXiv:2003.05037 (2020)."},{"key":"372_CR3","doi-asserted-by":"publisher","unstructured":"Song, Y. et al. Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images. medRxiv https:\/\/doi.org\/10.1101\/2020.02.23.20026930 (2020).","DOI":"10.1101\/2020.02.23.20026930"},{"key":"372_CR4","doi-asserted-by":"publisher","unstructured":"Wang, S. et al. 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