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Therefore, it is critical to find effective methods to identify patients with high risk of POD rapidly. Creating a fully automated score based on an automated machine\u2010learning algorithm may be a method to predict the incidence of POD quickly.<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and methods<\/jats:title><jats:p>This is the secondary analysis of an observational study, including 531\u00a0surgical patients who underwent general anesthesia. The least absolute shrinkage and selection operator (LASSO) was used to screen essential features associated with POD. Finally, eight features (age, intraoperative blood loss, anesthesia duration, extubation time, intensive care unit [ICU] admission, mini\u2010mental state examination score [MMSE], Charlson comorbidity index [CCI], postoperative neutrophil\u2010to\u2010lymphocyte ratio [NLR]) were used to established models. Four models, logistic regression, random forest, extreme gradient boosted trees, and support vector machines, were built in a training set (70% of participants) and evaluated in the remaining testing sample (30% of participants). Multivariate logistic regression analysis was used to explore independent risk factors for POD further.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Model 1 (logistic regression model) was found to outperform other classifier models in testing data (area under the curve [AUC] of 80.44%, 95% confidence interval [CI] 72.24%\u201388.64%) and achieve the lowest Brier Score as well. These variables including age (OR\u00a0=\u00a01.054, 95%CI: 1.017~1.093), extubation time (OR\u00a0=\u00a01.027, 95%CI: 1.012~1.044), ICU admission (OR\u00a0=\u00a02.238, 95%CI: 1.313~3.793), MMSE (OR\u00a0=\u00a00.929, 95%CI: 0.876~0.984), CCI (OR\u00a0=\u00a01.197, 95%CI: 1.038~1.384), and postoperative NLR (OR\u00a0=\u00a01.029, 95%CI: 1.002~1.057) were independent risk factors for POD in this study.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>We have built and validated a high\u2010performing algorithm to demonstrate the extent to which patient risk changes of POD during the perioperative period, thus leading to a rational therapeutic choice.<\/jats:p><\/jats:sec>","DOI":"10.1111\/cns.13758","type":"journal-article","created":{"date-parts":[[2021,11,18]],"date-time":"2021-11-18T15:47:38Z","timestamp":1637250458000},"page":"608-618","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Automated machine learning\u2010based model predicts postoperative delirium using readily extractable perioperative collected electronic data"],"prefix":"10.1111","volume":"28","author":[{"given":"Xiao\u2010Yi","family":"Hu","sequence":"first","affiliation":[{"name":"Department of Anesthesiology The Affiliated Hospital of Xuzhou Medical University  Jiangsu Province Xuzhou City China"},{"name":"Jiangsu Province Key Laboratory of Anesthesiology &amp; 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