The research aims to explore the use of Raman spectroscopy and machine learning to identify early biomarkers related to COVID-19 disease severity and mortality.
Utilized Raman spectroscopy for label-free analysis of biological samples.
Applied machine learning algorithms to model and analyze the data.
Focused on patient stratification at admission to predict outcomes.
Identified distinct early biomarkers linked to varying disease severity.
Demonstrated feasibility of individualizing care based on these biomarkers.
Suggested a potential increase in survival rates through improved stratification.
Abstract
These results suggest the potential of Raman spectroscopy and machine learning modeling to stratify COVID-19 patients at admission, individualize care, and improve survival rates.