Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods.
Journal
medRxiv : the preprint server for health sciences
Titre abrégé: medRxiv
Pays: United States
ID NLM: 101767986
Informations de publication
Date de publication:
09 Jul 2021
09 Jul 2021
Historique:
entrez:
16
7
2021
pubmed:
17
7
2021
medline:
17
7
2021
Statut:
epublish
Résumé
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
Identifiants
pubmed: 34268522
doi: 10.1101/2021.07.07.21259699
pmc: PMC8282111
pii:
doi:
Types de publication
Preprint
Langues
eng
Subventions
Organisme : NIGMS NIH HHS
ID : R25 GM067110
Pays : United States
Organisme : Intramural NIH HHS
ID : Z01 NS003031
Pays : United States
Commentaires et corrections
Type : UpdateIn
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