Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods Article Swipe
Farshad Saberi-Movahed
,
Mahyar Mohammadifard
,
Adel Mehrpooya
,
Mohammad Rezaei-Ravari
,
Kamal Berahmand
,
Mehrdad Rostami
,
Saeed Karami
,
Mohammad Najafzadeh
,
Davood Hajinezhad
,
Mina Jamshidi
,
Farshid Abedi
,
Mahtab Mohammadifard
,
Elnaz Farbod
,
Farinaz Safavi
,
Mohammadreza Dorvash
,
Shahrzad Vahedi
,
Mahdi Eftekhari
,
Farid Saberi-Movahed
,
Iman Tavassoly
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.07.07.21259699
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.07.07.21259699
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) O 2 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.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2021.07.07.21259699
- https://www.medrxiv.org/content/medrxiv/early/2021/07/09/2021.07.07.21259699.full.pdf
- OA Status
- green
- Cited By
- 25
- References
- 134
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3180846963
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3180846963Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2021.07.07.21259699Digital Object Identifier
- Title
-
Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection MethodsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-07-09Full publication date if available
- Authors
-
Farshad Saberi-Movahed, Mahyar Mohammadifard, Adel Mehrpooya, Mohammad Rezaei-Ravari, Kamal Berahmand, Mehrdad Rostami, Saeed Karami, Mohammad Najafzadeh, Davood Hajinezhad, Mina Jamshidi, Farshid Abedi, Mahtab Mohammadifard, Elnaz Farbod, Farinaz Safavi, Mohammadreza Dorvash, Shahrzad Vahedi, Mahdi Eftekhari, Farid Saberi-Movahed, Iman TavassolyList of authors in order
- Landing page
-
https://doi.org/10.1101/2021.07.07.21259699Publisher landing page
- PDF URL
-
https://www.medrxiv.org/content/medrxiv/early/2021/07/09/2021.07.07.21259699.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.medrxiv.org/content/medrxiv/early/2021/07/09/2021.07.07.21259699.full.pdfDirect OA link when available
- Concepts
-
Triage, Feature selection, Computer science, Biomarker, Random forest, Coronavirus disease 2019 (COVID-19), Medicine, Factorization, Intensive care medicine, Artificial intelligence, Emergency medicine, Algorithm, Disease, Internal medicine, Biochemistry, Chemistry, Infectious disease (medical specialty)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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25Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2023: 4, 2022: 13, 2021: 7Per-year citation counts (last 5 years)
- References (count)
-
134Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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