Applying a transformer architecture to intraoperative temporal dynamics improves the prediction of postoperative delirium
Abstract Background Patients who experienced postoperative delirium (POD) are at higher risk of poor outcomes like dementia or death. Previous machine learning models predicting POD mostly relied on time-aggregated features. We aimed to assess the potential of temporal patterns in clinical parameter...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-11-01
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| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-024-00681-x |
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