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Machine learning approach to predicting COVID-19 disease severity based on clinical blood test data: Statistical analysis and model development

Aktar, S, Ahamad, MM, Rashed-Al-Mahfuz, M, Azad, AKM, Uddin, S, Kamal, AHM, Alyami, SA, Lin, PI, Shariful Islam, Sheikh, Quinn, JMW, Eapen, V and Moni, MA 2021, Machine learning approach to predicting COVID-19 disease severity based on clinical blood test data: Statistical analysis and model development, JMIR Medical Informatics, vol. 9, no. 4, pp. 1-15, doi: 10.2196/25884.

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Title Machine learning approach to predicting COVID-19 disease severity based on clinical blood test data: Statistical analysis and model development
Author(s) Aktar, S
Ahamad, MM
Rashed-Al-Mahfuz, M
Azad, AKM
Uddin, S
Kamal, AHM
Alyami, SA
Lin, PI
Shariful Islam, SheikhORCID iD for Shariful Islam, Sheikh orcid.org/0000-0001-7926-9368
Quinn, JMW
Eapen, V
Moni, MA
Journal name JMIR Medical Informatics
Volume number 9
Issue number 4
Article ID e25884
Start page 1
End page 15
Total pages 15
Publisher J M I R Publications
Place of publication Toronto, Canada
Publication date 2021-04
ISSN 2291-9694
2291-9694
Keyword(s) blood
blood samples
COMPLEXITIES
COVID-19
data set
Life Sciences & Biomedicine
machine learning
Medical Informatics
morbidity
mortality
outcome
prediction
risk
Science & Technology
severity
statistical analysis
testing
WUHAN
Language eng
DOI 10.2196/25884
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30149739

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.