Explanatory predictive model for COVID-19 severity risk employing machine learning, shapley addition, and LIME.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
04 04 2023
04 04 2023
Historique:
received:
27
11
2022
accepted:
14
03
2023
medline:
6
4
2023
entrez:
4
4
2023
pubmed:
5
4
2023
Statut:
epublish
Résumé
The rapid spread of SARS-CoV-2 threatens global public health and impedes the operation of healthcare systems. Several studies have been conducted to confirm SARS-CoV-2 infection and examine its risk factors. To produce more effective treatment options and vaccines, it is still necessary to investigate biomarkers and immune responses in order to gain a deeper understanding of disease pathophysiology. This study aims to determine how cytokines influence the severity of SARS-CoV-2 infection. We measured the plasma levels of 48 cytokines in the blood of 87 participants in the COVID-19 study. Several Classifiers were trained and evaluated using Machine Learning and Deep Learning to complete missing data, generate synthetic data, and fill in any gaps. To examine the relationship between cytokine storm and COVID-19 severity in patients, the Shapley additive explanation (SHAP) and the LIME (Local Interpretable Model-agnostic Explanations) model were applied. Individuals with severe SARS-CoV-2 infection had elevated plasma levels of VEGF-A, MIP-1b, and IL-17. RANTES and TNF were associated with healthy individuals, whereas IL-27, IL-9, IL-12p40, and MCP-3 were associated with non-Severity. These findings suggest that these cytokines may promote the development of novel preventive and therapeutic pathways for disease management. In this study, the use of artificial intelligence is intended to support clinical diagnoses of patients to determine how each cytokine may be responsible for the severity of COVID-19, which could lead to the identification of several cytokines that could aid in treatment decision-making and vaccine development.
Identifiants
pubmed: 37015978
doi: 10.1038/s41598-023-31542-7
pii: 10.1038/s41598-023-31542-7
pmc: PMC10071246
doi:
Substances chimiques
lime
C7X2M0VVNH
Cytokines
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5481Informations de copyright
© 2023. The Author(s).
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