A Machine Learning Algorithm for Quantitatively Diagnosing Oxidative Stress Risks in Healthy Adult Individuals Based on Health Space Methodology: A Proof-of-Concept Study Using Korean Cross-Sectional Cohort Data.
composite biomarker
diagnostic model
elastic net regularized generalized linear model
oxidative stress
Journal
Antioxidants (Basel, Switzerland)
ISSN: 2076-3921
Titre abrégé: Antioxidants (Basel)
Pays: Switzerland
ID NLM: 101668981
Informations de publication
Date de publication:
16 Jul 2021
16 Jul 2021
Historique:
received:
26
05
2021
revised:
14
07
2021
accepted:
14
07
2021
entrez:
6
8
2021
pubmed:
7
8
2021
medline:
7
8
2021
Statut:
epublish
Résumé
Oxidative stress aggravates the progression of lifestyle-related chronic diseases. However, knowledge and practices that enable quantifying oxidative stress are still lacking. Here, we performed a proof-of-concept study to predict the oxidative stress status in a healthy population using retrospective cohort data from Boramae medical center in Korea (
Identifiants
pubmed: 34356365
pii: antiox10071132
doi: 10.3390/antiox10071132
pmc: PMC8301183
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : National Research Foundation of Korea
ID : 2012M3A9C4048761
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