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Decoding clinical biomarker space of COVID-19 : exploring matrix factorization-based feature selection methods |
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| Author: | Saberi-Movahed, Farshad1; Mohammadifard, Mahyar2; Mehrpooya, Adel3; |
| Organizations: |
1College of Engineering, North Carolina State University, Raleigh, NC, 22606, USA 2Department of Radiology, Birjand University of Medical Sciences, Birjand, Iran 3School of Mathematical Sciences, Science and Engineering Faculty, Queensland University of Technology (QUT), Brisbane, Australia
4Department of Computer Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
5School of Computer Science, Faculty of Science, Queensland University of Technology (QUT), Brisbane, Australia 6Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, Oulu, Finland 7Department of Mathematics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, 45137-66731, Iran 8Department of Applied Mathematics, Faculty of Sciences and Modern Technologies, Graduate University of Advanced Technology, Kerman, Iran 9SAS Institute Inc., Cary, NC, USA 10Infectious Diseases Research Center, Birjand University of Medical Sciences, Birjand, Iran 11Department of Pathology, Birjand University of Medical Sciences, Birjand, Iran 12Baruch College, City University of New York, New York, USA 13Neuroimmunology and Neurovirology Branch, National Institute of Neurological Disorders and Stroke, National Institute of Health, Bethesda, MD, USA 14Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Viewbank, VIC, Australia 15Department of Pharmacognosy and Pharmaceutical Biotechnology, School of Pharmacy, Iran University of Medical Sciences, Tehran, Iran 16Independent Researcher, Vancouver, Canada 17BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia 18Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 64002, Taiwan 19Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY10029, USA |
| Format: | article |
| Version: | accepted version |
| Access: | open |
| Online Access: | PDF Full Text (PDF, 0.6 MB) |
| Persistent link: | http://urn.fi/urn:nbn:fi-fe2023040434973 |
| Language: | English |
| Published: |
Elsevier,
2022
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| Publish Date: | 2023-04-05 |
| Description: |
AbstractOne of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O₂ Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases. see all
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| Series: |
Computers in biology and medicine |
| ISSN: | 0010-4825 |
| ISSN-E: | 1879-0534 |
| ISSN-L: | 0010-4825 |
| Volume: | 146 |
| Article number: | 105426 |
| DOI: | 10.1016/j.compbiomed.2022.105426 |
| OADOI: | https://oadoi.org/10.1016/j.compbiomed.2022.105426 |
| Type of Publication: |
A1 Journal article – refereed |
| Field of Science: |
113 Computer and information sciences 318 Medical biotechnology |
| Subjects: | |
| Funding: |
I.T. contributed to this paper at the Cellular Energetics Program of the Kavli Institute for Theoretical Physics, supported in part by the National Science Foundation Grant NSF PHY-1748958, NIH Grant R25GM067110, and the Gordon and Betty Moore Foundation Grant 2919.02. This work was partly supported by the Intramural Research Program of the National Institute of Neurological Disorder and Stroke/National Institutes of Health (Grant NS003031) (to F.S.). Also, this work is supported by the Academy of Finland Profi5 (Project number 326291) on DigiHealth, which gratefully acknowledged (to M.R). |
| Copyright information: |
© 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ |
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https://creativecommons.org/licenses/by-nc-nd/4.0/ |