Automated tracking of level of consciousness and delirium in critical illness using deep learning
| Autor: | Haoqi Sun, Emily J. Boyle, Oluwaseun Akeju, M. Brandon Westover, Wendong Ge, Eyal Y. Kimchi, David W. Zhou, Wei-Long Zheng, Lauren M. McClain, Sunil B. Nagaraj |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
medicine.medical_specialty
Sedation Medicine (miscellaneous) Health Informatics Disorders of consciousness ICU PATIENTS Electroencephalography lcsh:Computer applications to medicine. Medical informatics Article CLASSIFICATION VALIDATION 03 medical and health sciences 0302 clinical medicine Level of consciousness Health Information Management medicine 030212 general & internal medicine EEG Elective surgery VALIDITY medicine.diagnostic_test business.industry Deep learning Diagnostic markers Translational research medicine.disease Computer Science Applications TIME AGITATION-SEDATION SCALE Critical illness Emergency medicine RELIABILITY ELECTROENCEPHALOGRAPHY Delirium lcsh:R858-859.7 Artificial intelligence medicine.symptom business 030217 neurology & neurosurgery |
| Zdroj: | Npj digital medicine, 2:89. Nature Publishing Group NPJ Digital Medicine npj Digital Medicine, Vol 2, Iss 1, Pp 1-8 (2019) |
| ISSN: | 2398-6352 |
| Popis: | Over- and under-sedation are common in the ICU, and contribute to poor ICU outcomes including delirium. Behavioral assessments, such as Richmond Agitation-Sedation Scale (RASS) for monitoring levels of sedation and Confusion Assessment Method for the ICU (CAM-ICU) for detecting signs of delirium, are often used. As an alternative, brain monitoring with electroencephalography (EEG) has been proposed in the operating room, but is challenging to implement in ICU due to the differences between critical illness and elective surgery, as well as the duration of sedation. Here we present a deep learning model based on a combination of convolutional and recurrent neural networks that automatically tracks both the level of consciousness and delirium using frontal EEG signals in the ICU. For level of consciousness, the system achieves a median accuracy of 70% when allowing prediction to be within one RASS level difference across all patients, which is comparable or higher than the median technician–nurse agreement at 59%. For delirium, the system achieves an AUC of 0.80 with 69% sensitivity and 83% specificity at the optimal operating point. The results show it is feasible to continuously track level of consciousness and delirium in the ICU. |
| Databáze: | OpenAIRE |
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