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, A; Uddin, S; Kamal, A; Alyami, SA; Lin, P-I; Islam, SMS; Quinn, JM; Eapen, V; Moni, MA

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, A Uddin, S Kamal, A Alyami, SA Lin, P-I Islam, SMS Quinn, JM Eapen, V Moni, MA

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Publisher:: JMIR Publications
Publication Type:: Journal Article
Citation:: JMIR Medical Informatics, 2021, 9, (4), pp. 1-15
Issue Date:: 2021-04-13

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Field	Value	Language
dc.contributor.author	Aktar, S
dc.contributor.author	Ahamad, MM
dc.contributor.author	Rashed-Al-Mahfuz, M
dc.contributor.author	Azad, A
dc.contributor.author	Uddin, S
dc.contributor.author	Kamal, A
dc.contributor.author	Alyami, SA
dc.contributor.author	Lin, P-I
dc.contributor.author	Islam, SMS
dc.contributor.author	Quinn, JM
dc.contributor.author	Eapen, V
dc.contributor.author	Moni, MA
dc.date.accessioned	2022-04-28T01:44:30Z
dc.date.available	2021-03-21
dc.date.available	2022-04-28T01:44:30Z
dc.date.issued	2021-04-13
dc.identifier.citation	JMIR Medical Informatics, 2021, 9, (4), pp. 1-15
dc.identifier.issn	2291-9694
dc.identifier.issn	2291-9694
dc.identifier.uri	http://hdl.handle.net/10453/156733
dc.description.abstract	BACKGROUND: Accurate prediction of the disease severity of patients with COVID-19 would greatly improve care delivery and resource allocation and thereby reduce mortality risks, especially in less developed countries. Many patient-related factors, such as pre-existing comorbidities, affect disease severity and can be used to aid this prediction. OBJECTIVE: Because rapid automated profiling of peripheral blood samples is widely available, we aimed to investigate how data from the peripheral blood of patients with COVID-19 can be used to predict clinical outcomes. METHODS: We investigated clinical data sets of patients with COVID-19 with known outcomes by combining statistical comparison and correlation methods with machine learning algorithms; the latter included decision tree, random forest, variants of gradient boosting machine, support vector machine, k-nearest neighbor, and deep learning methods. RESULTS: Our work revealed that several clinical parameters that are measurable in blood samples are factors that can discriminate between healthy people and COVID-19-positive patients, and we showed the value of these parameters in predicting later severity of COVID-19 symptoms. We developed a number of analytical methods that showed accuracy and precision scores >90% for disease severity prediction. CONCLUSIONS: We developed methodologies to analyze routine patient clinical data that enable more accurate prediction of COVID-19 patient outcomes. With this approach, data from standard hospital laboratory analyses of patient blood could be used to identify patients with COVID-19 who are at high risk of mortality, thus enabling optimization of hospital facilities for COVID-19 treatment.
dc.format	Electronic
dc.language	eng
dc.publisher	JMIR Publications
dc.relation.ispartof	JMIR Medical Informatics
dc.relation.isbasedon	10.2196/25884
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Machine Learning Approach to Predicting COVID-19 Disease Severity Based on Clinical Blood Test Data: Statistical Analysis and Model Development.
dc.type	Journal Article
utslib.citation.volume	9
utslib.location.activity	Canada
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2022-04-28T01:44:29Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	9
utslib.citation.issue	4

Abstract:

BACKGROUND: Accurate prediction of the disease severity of patients with COVID-19 would greatly improve care delivery and resource allocation and thereby reduce mortality risks, especially in less developed countries. Many patient-related factors, such as pre-existing comorbidities, affect disease severity and can be used to aid this prediction. OBJECTIVE: Because rapid automated profiling of peripheral blood samples is widely available, we aimed to investigate how data from the peripheral blood of patients with COVID-19 can be used to predict clinical outcomes. METHODS: We investigated clinical data sets of patients with COVID-19 with known outcomes by combining statistical comparison and correlation methods with machine learning algorithms; the latter included decision tree, random forest, variants of gradient boosting machine, support vector machine, k-nearest neighbor, and deep learning methods. RESULTS: Our work revealed that several clinical parameters that are measurable in blood samples are factors that can discriminate between healthy people and COVID-19-positive patients, and we showed the value of these parameters in predicting later severity of COVID-19 symptoms. We developed a number of analytical methods that showed accuracy and precision scores >90% for disease severity prediction. CONCLUSIONS: We developed methodologies to analyze routine patient clinical data that enable more accurate prediction of COVID-19 patient outcomes. With this approach, data from standard hospital laboratory analyses of patient blood could be used to identify patients with COVID-19 who are at high risk of mortality, thus enabling optimization of hospital facilities for COVID-19 treatment.

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http://hdl.handle.net/10453/156733