About: covid:ec7b278df21167ff8ff328d70f6cd2332636dd30

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An Entity of Type : owl:Thing, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
Creator	Baker, Mark Banerjee, Abhirup Kitson, Joanne Mackenzie, Louise Mamalakis, Michail Ray, Surajit Vorselaars, Bart Weeks, Simonne
is schema:about of	named entity 'SARS-CoV-2' named entity 'SARS-CoV-2' named entity 'artificial neural network' named entity 'standard deviation' named entity 'SARS-CoV-2' named entity 'SEEN' named entity 'MACHINE LEARNING' named entity 'PANDEMIC' named entity 'PREDICT' named entity 'SIMPLE' named entity 'STANDARD DEVIATION' named entity 'HAVE' named entity 'AIM' named entity 'INDIVIDUALS' named entity 'TRAINING' named entity 'HISTORY OF' named entity 'ZERO' named entity 'RESULTS' named entity 'COVID-19' named entity 'OVERLAP WITH' named entity 'SARS-CoV-2' named entity 'statistical test' named entity 'ways' named entity 'SARS-CoV-2' named entity 'hospital' named entity 'hospital' named entity 'machine learning' named entity 'Hospital Israelita Albert Einstein' named entity 'dataset' named entity 'Artificial Intelligence' named entity 'Counts' named entity 'standard deviation' named entity 'Hospital Israelita Albert Einstein' named entity 'coronavirus' named entity 'statistical test' named entity 'Influenza' named entity 'COVID-19' named entity 'clinical data' named entity 'SARS-CoV-2' named entity 'SARS-CoV-2' named entity 'macrophage' named entity 'health care' named entity 'cough' named entity 'SARS-CoV-2' named entity 'data based' named entity 'RBC' named entity 'pathogens' named entity 'pathogens' named entity 'blood counts' named entity 'monocytes' named entity 'random forest' named entity 'monocyte' named entity 'Coronavirus NL63' named entity 'H1N1' named entity 'shallow learning' named entity 'Oversampling' named entity 'COVID' named entity 'blood counts' named entity 'pathogens' named entity 'SARS-CoV-2' named entity 'leukocyte' named entity 'monocytes' named entity 'basophils' named entity 'immune response' named entity 'peritonitis' named entity 'eosinophils' named entity 'global challenge' named entity 'haemoglobin' named entity 'random forest' named entity 'COVID-19 pandemic' named entity 'probability' named entity 'logistic regression' named entity 'ANN' named entity 'lymphopenia' named entity 'Coronaviruses' named entity 'SARS-CoV-2' named entity 'pathogen' named entity 'Leukocytes' named entity 'neutrophils' named entity 'sensitivity, specificity' named entity 'monocyte' named entity 'monocytes' named entity 'immune response' named entity 'monocyte' named entity 'monocytes' named entity 'blood count' named entity 'random forest' named entity 'SARS-CoV-2' named entity 'coronavirus' named entity 'platelets' named entity 'SARS-CoV-2' named entity 'blood count'

Attributes

Values

Creator

Baker, Mark
Banerjee, Abhirup
Kitson, Joanne
Mackenzie, Louise
Mamalakis, Michail
Ray, Surajit
Vorselaars, Bart
Weeks, Simonne

is schema:about of

named entity 'SARS-CoV-2'
named entity 'SARS-CoV-2'
named entity 'artificial neural network'
named entity 'standard deviation'
named entity 'SARS-CoV-2'
named entity 'SEEN'
named entity 'MACHINE LEARNING'
named entity 'PANDEMIC'
named entity 'PREDICT'
named entity 'SIMPLE'
named entity 'STANDARD DEVIATION'
named entity 'HAVE'
named entity 'AIM'
named entity 'INDIVIDUALS'
named entity 'TRAINING'
named entity 'HISTORY OF'
named entity 'ZERO'
named entity 'RESULTS'
named entity 'COVID-19'
named entity 'OVERLAP WITH'
named entity 'SARS-CoV-2'
named entity 'statistical test'
named entity 'ways'
named entity 'SARS-CoV-2'
named entity 'hospital'
named entity 'hospital'
named entity 'machine learning'
named entity 'Hospital Israelita Albert Einstein'
named entity 'dataset'
named entity 'Artificial Intelligence'
named entity 'Counts'
named entity 'standard deviation'
named entity 'Hospital Israelita Albert Einstein'
named entity 'coronavirus'
named entity 'statistical test'
named entity 'Influenza'
named entity 'COVID-19'
named entity 'clinical data'
named entity 'SARS-CoV-2'
named entity 'SARS-CoV-2'
named entity 'macrophage'
named entity 'health care'
named entity 'cough'
named entity 'SARS-CoV-2'
named entity 'data based'
named entity 'RBC'
named entity 'pathogens'
named entity 'pathogens'
named entity 'blood counts'
named entity 'monocytes'
named entity 'random forest'
named entity 'monocyte'
named entity 'Coronavirus NL63'
named entity 'H1N1'
named entity 'shallow learning'
named entity 'Oversampling'
named entity 'COVID'
named entity 'blood counts'
named entity 'pathogens'
named entity 'SARS-CoV-2'
named entity 'leukocyte'
named entity 'monocytes'
named entity 'basophils'
named entity 'immune response'
named entity 'peritonitis'
named entity 'eosinophils'
named entity 'global challenge'
named entity 'haemoglobin'
named entity 'random forest'
named entity 'COVID-19 pandemic'
named entity 'probability'
named entity 'logistic regression'
named entity 'ANN'
named entity 'lymphopenia'
named entity 'Coronaviruses'
named entity 'SARS-CoV-2'
named entity 'pathogen'
named entity 'Leukocytes'
named entity 'neutrophils'
named entity 'sensitivity, specificity'
named entity 'monocyte'
named entity 'monocytes'
named entity 'immune response'
named entity 'monocyte'
named entity 'monocytes'
named entity 'blood count'
named entity 'random forest'
named entity 'SARS-CoV-2'
named entity 'coronavirus'
named entity 'platelets'
named entity 'SARS-CoV-2'
named entity 'blood count'

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