ArticleScientific reports2023
Supervised deep learning with vision transformer predicts delirium using limited lead EEG.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 25 citations in OpenAlex.
- Artificial Intelligence-Based Delirium Prediction Model for Post-Cardiac Surgery Patients: A Scoping Review.Journal of advanced nursing · 2026Article
- Prediction Models for Postoperative Delirium Among Cancer Patients: A Scoping Review.Healthcare (Basel, Switzerland) · 2026Review
- Predicting delirium in critically Ill COVID-19 patients using EEG-derived data: a machine learning approach.GeroScience · 2026Observational
- Machine learning and artificial intelligence for delirium prediction with Electronic Health Records (EHR): a scoping review.BMC medical informatics and decision making · 2026Article
- Bedside EEG for rapid diagnosis of delirium.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2026Review
- Research advances on acute kidney injury and brain dysfunction.Frontiers in nephrology · 2026Review
- Development of a deep learning-based prediction model for postoperative delirium using intraoperative electroencephalogram in adults.NPJ digital medicine · 2025Article
- Point-of-Care Electroencephalography in Acute Neurological Care: A Narrative Review.Neurology international · 2025Review
- Transformers in EEG Analysis: A Review of Architectures and Applications in Motor Imagery, Seizure, and Emotion Classification.Sensors (Basel, Switzerland) · 2025Review
- Improving diagnostic accuracy of routine EEG for epilepsy using deep learning.Brain communications · 2025Article
- Differentiating Epileptic and Psychogenic Non-Epileptic Seizures Using Machine Learning Analysis of EEG Plot Images.Sensors (Basel, Switzerland) · 2024Article
- Utilizing portable electroencephalography to screen for pathology of Alzheimer's disease: a methodological advancement in diagnosis of neurodegenerative diseases.Frontiers in psychiatry · 2024Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
As many as 80% of critically ill patients develop delirium increasing the need for institutionalization and higher morbidity and mortality. Clinicians detect less than 40% of delirium when using a validated screening tool. EEG is the criterion standard but is resource intensive thus not feasible for widespread delirium monitoring. This study evaluated the use of limited-lead rapid-response EEG and supervised deep learning methods with vision transformer to predict delirium. This proof-of-concept study used a prospective design to evaluate use of supervised deep learning with vision transformer and a rapid-response EEG device for predicting delirium in mechanically ventilated critically ill older adults. Fifteen different models were analyzed. Using all available data, the vision transformer models provided 99.9%+ training and 97% testing accuracy across models. Vision transformer with rapid-response EEG is capable of predicting delirium. Such monitoring is feasible in critically ill older adults. Therefore, this method has strong potential for improving the accuracy of delirium detection, providing greater opportunity for individualized interventions. Such an approach may shorten hospital length of stay, increase discharge to home, decrease mortality, and reduce the financial burden associated with delirium.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.