University of Oulu

Saberi-Movahed, F., Mohammadifard, M., Mehrpooya, A., Rezaei-Ravari, M., Berahmand, K., Rostami, M., Karami, S., Najafzadeh, M., Hajinezhad, D., Jamshidi, M., Abedi, F., Mohammadifard, M., Farbod, E., Safavi, F., Dorvash, M., Mottaghi-Dastjerdi, N., Vahedi, S., Eftekhari, M., Saberi-Movahed, F., … Tavassoly, I. (2022). Decoding clinical biomarker space of COVID-19: Exploring matrix factorization-based feature selection methods. Computers in Biology and Medicine, 146, 105426.

Decoding clinical biomarker space of COVID-19 : exploring matrix factorization-based feature selection methods

Saved in:
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:
Language: English
Published: Elsevier, 2022
Publish Date: 2023-04-05


One 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

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
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
318 Medical biotechnology
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