ArticleJournal of biomedical optics2026
Combining label-free Raman spectroscopy and machine learning to identify early biomarkers of COVID-19 disease severity and mortality.
Article in Journal of biomedical optics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Combining label-free Raman spectroscopy with machine learning to monitor COVID-19 disease from acute infection to recovery.Journal of biomedical optics · 2026Article
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Authors and funding
16 authors.
Funding
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Abstract
Significance: Early prediction of COVID-19 severity and mortality is crucial for optimizing clinical care and patient outcomes, but remains challenging. Aim: We aim to develop a screening tool combining label-free Raman spectroscopy and machine learning modeling to predict COVID-19 severity and mortality. Approach: Patients infected by SARS-CoV-2 ( Results: Raman peaks assigned to proteins, glucose, lactic acid, fatty acids, urea, and lipids were extracted by the models. Area under the receiver operating characteristic curve ranged between 0.83 and 0.94, with sensitivities and specificities ranging between 80% and 83% and 75% and 92%, respectively. Accuracy for detecting mortality, invasive ventilation, and critical disease was 90%, 87%, and 78%. A complementary metabolomic analysis confirmed some molecular differences between groups. Conclusions: 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.
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