Supervised Deep Learning with Vision Transformer Predicts Delirium Using Limited Lead EEG

Malissa Mulkey; Huyunting Huang; Thomas Albanese; Sunghan Kim; Baijian Yang

doi:10.1038/s41598-023-35004-y">https://doi.org/10.1038/s41598-023-35004-y</a></p

May 16, 2023Open Access

Supervised Deep Learning with Vision Transformer Predicts Delirium Using Limited Lead EEG

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Key Points

To investigate the relationship between biomarkers and the occurrence of delirium and coma in COVID-19 ICU patients.
Analyzed biomarkers of inflammation and hypercoagulability in ICU patients with COVID-19.
Measured daily concentrations of CRP, d-dimer, and ferritin.
Assessed delirium and coma using established assessment scales.
Conducted retrospective analysis of recorded data from ICU admissions.
Higher d-dimer and ferritin levels significantly increased the odds of next day delirium/coma.
d-dimer was linked to next day delirium and coma with specific odds ratios.
Ferritin quartiles were significantly associated with the likelihood of experiencing delirium and coma independently.
CRP levels were associated with increased delirium severity and the risk of coma.

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Implication

Retrospective cohort study finds biomarkers predict outcomes of delirium and coma in critically ill COVID-19 patients, suggesting potential monitoring strategies.

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Mulkey et al. (2023) studied this question.

synapsesocial.com/papers/69be361e6e48c4981c674bbf https://doi.org/10.1038/s41598-023-35004-y">https://doi.org/10.1038/s41598-023-35004-y</a></p

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Supervised Deep Learning with Vision Transformer Predicts Delirium Using Limited Lead EEG

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No abstract yet

Implication

Discussion

Cite This Study

Supervised Deep Learning with Vision Transformer Predicts Delirium Using Limited Lead EEG

Key Points

No abstract yet

Implication

Discussion

Cite This Study

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