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Please use this identifier to cite or link to this item: http://hdl.handle.net/11375/31368
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DC FieldValueLanguage
dc.contributor.authorSnider B-
dc.contributor.authorPhillips P-
dc.contributor.authorMacLean A-
dc.contributor.authorMcBean E-
dc.contributor.authorGadsden SA-
dc.contributor.authorYawney J-
dc.date.accessioned2025-03-03T23:33:06Z-
dc.date.available2025-03-03T23:33:06Z-
dc.date.issued2020-09-30-
dc.identifier.urihttp://hdl.handle.net/11375/31368-
dc.subject32 Biomedical and Clinical Sciences-
dc.subject3202 Clinical Sciences-
dc.subject42 Health Sciences-
dc.subjectMachine Learning and Artificial Intelligence-
dc.subjectEmerging Infectious Diseases-
dc.subjectCoronaviruses-
dc.subjectBioengineering-
dc.subjectInfectious Diseases-
dc.subject3 Good Health and Well Being-
dc.titleArtificial intelligence to predict the risk of mortality from Covid-19: Insights from a Canadian Application-
dc.typeArticle-
dc.date.updated2025-03-03T23:33:04Z-
dc.contributor.departmentMechanical Engineering-
dc.identifier.doihttps://doi.org/10.1101/2020.09.29.20201632-
Appears in Collections:Mechanical Engineering Publications

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