Using machine learning in prediction of ICU admission, mortality, and length of stay in the early stage of admission of COVID-19 patients. | D2R Server publishing the DBLP Bibliography Database, hosted at L3S Research Center

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dc:creator	<https://dblp.l3s.de/d2r/resource/authors/Alex_Kuiper>
dc:creator	<https://dblp.l3s.de/d2r/resource/authors/Khodakaram_Salimifard>
dc:creator	<https://dblp.l3s.de/d2r/resource/authors/Maryam_Marzban>
dc:creator	<https://dblp.l3s.de/d2r/resource/authors/Reza_Mohammadi_0002>
dc:creator	<https://dblp.l3s.de/d2r/resource/authors/Sara_Saadatmand>
foaf:homepage	<http://dx.doi.org/doi.org%2F10.1007%2Fs10479-022-04984-x>
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dc:identifier	DBLP journals/anor/SaadatmandSMKMF23 (xsd:string)
dc:identifier	DOI doi.org%2F10.1007%2Fs10479-022-04984-x (xsd:string)
dcterms:issued	2023 (xsd:gYear)
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rdfs:label	Using machine learning in prediction of ICU admission, mortality, and length of stay in the early stage of admission of COVID-19 patients. (xsd:string)
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dc:title	Using machine learning in prediction of ICU admission, mortality, and length of stay in the early stage of admission of COVID-19 patients. (xsd:string)
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