{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T09:01:37Z","timestamp":1790240497002,"version":"4.1.0"},"reference-count":60,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2023,4,30]],"date-time":"2023-04-30T00:00:00Z","timestamp":1682812800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["CNS Neurosci Ther"],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Aims<\/jats:title><jats:p>Machine learning\u2010based identification of key variables and prediction of postoperative delirium in patients with extensive burns.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Five hundred and eighteen patients with extensive burns who underwent surgery were included and randomly divided into a training set, a validation set, and a testing set. Multifactorial logistic regression analysis was used to screen for significant variables. Nine prediction models were constructed in the training and validation sets (80% of dataset). The testing set (20% of dataset) was used to further evaluate the model. The area under the receiver operating curve (AUROC) was used to compare model performance. SHapley Additive exPlanations (SHAP) was used to interpret the best one and to externally validate it in another large tertiary hospital.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Seven variables were used in the development of nine prediction models: physical restraint, diabetes, sex, preoperative hemoglobin, acute physiological and chronic health assessment, time in the Burn Intensive Care Unit and total body surface area. Random Forest (RF) outperformed the other eight models in terms of predictive performance (ROC:84.00%) When external validation was performed, RF performed well (accuracy: 77.12%, sensitivity: 67.74% and specificity: 80.46%).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The first machine learning\u2010based delirium prediction model for patients with extensive burns was successfully developed and validated. High\u2010risk patients for delirium can be effectively identified and targeted interventions can be made to reduce the incidence of delirium.<\/jats:p><\/jats:sec>","DOI":"10.1111\/cns.14237","type":"journal-article","created":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T05:48:40Z","timestamp":1682920120000},"page":"2986-2997","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Machine learning for prediction of delirium in patients with extensive burns after surgery"],"prefix":"10.1111","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6964-8735","authenticated-orcid":false,"given":"Yujie","family":"Ren","sequence":"first","affiliation":[{"name":"Medical Center of Burn Plastic and Wound Repair The First Affiliated Hospital of Nanchang University  Nanchang 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