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Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Postoperative delirium (POD) is associated with increased morbidity and mortality. This study aims to develop a deep learning-based model (DELPHI-EEG) to predict postoperative delirium using intraoperative electroencephalogram (EEG) waveform. A total of 34,550 surgical cases (267 event cases), with 6-lead intraoperative EEG monitoring between 2022 and 2024, were included for model development. During 5-fold cross-validation, the DELPHI-EEG model showed an area under the receiver operating characteristic (AUROC) curve of 0.870 (95% confidence interval [CI]: 0.789\u20130.935) and the area under the precision-recall curve (AUPRC) of 0.038 (95% CI: 0.017\u20130.084), significantly outperforming the logistic regression model using burst suppression ratio with AUROC of 0.729 (95% CI: 0.624\u20130.825,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20090.004) and AUPRC of 0.013 (95% CI: 0.007\u20130.026,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20090.002). The DELPHI-EEG model might serve as a risk predictor for postoperative delirium, potentially enabling targeted preventive interventions for surgical patients; nonetheless, external validation in diverse clinical settings is required.\n                  <\/jats:p>","DOI":"10.1038\/s41746-025-02033-y","type":"journal-article","created":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T21:52:43Z","timestamp":1763416363000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Development of a deep learning-based prediction model for postoperative delirium using intraoperative electroencephalogram in adults"],"prefix":"10.1038","volume":"8","author":[{"given":"Jang Ho","family":"Ahn","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyeonhoon","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pedro","family":"Gambus","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyun-Kyu","family":"Yoon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae-Woo","family":"Ju","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyung-Chul","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,17]]},"reference":[{"key":"2033_CR1","doi-asserted-by":"publisher","first-page":"4053","DOI":"10.2147\/IJGM.S349232","volume":"15","author":"A Mossie","year":"2022","unstructured":"Mossie, A. et al. 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