{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T23:45:40Z","timestamp":1791243940535,"version":"4.1.0"},"reference-count":72,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T00:00:00Z","timestamp":1723593600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosdigitalhealth.org"],"crossmark-restriction":false},"short-container-title":["PLOS Digit Health"],"abstract":"<jats:p>Postoperative delirium (POD) contributes to severe outcomes such as death or development of dementia. Thus, it is desirable to identify vulnerable patients in advance during the perioperative phase. Previous studies mainly investigated risk factors for delirium during hospitalization and further used a linear logistic regression (LR) approach with time-invariant data. Studies have not investigated patients\u2019 fluctuating conditions to support POD precautions. In this single-center study, we aimed to predict POD in a recovery room setting with a non-linear machine learning (ML) technique using pre-, intra-, and postoperative data. The target variable POD was defined with the Nursing Screening Delirium Scale (Nu-DESC) \u2265 1. Feature selection was conducted based on robust univariate test statistics and L<jats:sub>1<\/jats:sub> regularization. Non-linear multi-layer perceptron (MLP) as well as tree-based models were trained and evaluated\u2014with the receiver operating characteristics curve (AUROC), the area under precision recall curve (AUPRC), and additional metrics\u2014against LR and published models on bootstrapped testing data. The prevalence of POD was 8.2% in a sample of 73,181 surgeries performed between 2017 and 2020. Significant univariate impact factors were the preoperative ASA status (American Society of Anesthesiologists physical status classification system), the intraoperative amount of given remifentanil, and the postoperative Aldrete score. The best model used pre-, intra-, and postoperative data. The non-linear boosted trees model achieved a mean AUROC of 0.854 and a mean AUPRC of 0.418 outperforming linear LR, well as best applied and retrained baseline models. Overall, non-linear machine learning models using data from multiple perioperative time phases were superior to traditional ones in predicting POD in the recovery room. Class imbalance was seen as a main impediment for model application in clinical practice.<\/jats:p>","DOI":"10.1371\/journal.pdig.0000414","type":"journal-article","created":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T17:32:36Z","timestamp":1723656756000},"page":"e0000414","update-policy":"https:\/\/doi.org\/10.1371\/journal.pdig.corrections_policy","source":"Crossref","is-referenced-by-count":10,"title":["Predicting postoperative delirium assessed by the Nursing Screening Delirium Scale in the recovery room for non-cardiac surgeries without craniotomy: A retrospective study using a machine learning approach"],"prefix":"10.1371","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0808-3966","authenticated-orcid":true,"given":"Niklas","family":"Giesa","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Haufe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mario","family":"Menk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bj\u00f6rn","family":"Wei\u00df","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claudia D.","family":"Spies","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sophie K.","family":"Piper","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Felix","family":"Balzer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7993-7901","authenticated-orcid":true,"given":"Sebastian D.","family":"Boie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,8,14]]},"reference":[{"issue":"1","key":"pdig.0000414.ref001","doi-asserted-by":"crossref","first-page":"4","DOI":"10.4097\/kja.d.18.00073.1","article-title":"Postoperative delirium","volume":"72","author":"ST Oh","year":"2019","journal-title":"Korean J Anesthesiol"},{"issue":"1","key":"pdig.0000414.ref002","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1186\/s13063-018-3148-8","article-title":"Patient safety, cost-effectiveness, and quality of life: reduction of delirium risk and postoperative cognitive dysfunction after elective procedures in older adults\u2014study protocol for a stepped-wedge cluster randomized trial (PAWEL Study)","volume":"20","author":"PAWEL Study group","year":"2019","journal-title":"Trials"},{"issue":"5","key":"pdig.0000414.ref003","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1213\/ANE.0b013e3182147f6d","article-title":"Postoperative Delirium: Acute Change with Long-Term Implications","volume":"112","author":"JL Rudolph","year":"2011","journal-title":"Anesth Analg"},{"key":"pdig.0000414.ref004","article-title":"Implementation of strategies to prevent and treat postoperative delirium in the post-anesthesia caring unit","volume":"7","author":"T. Saller","journal-title":"J Clin Monit Comput"},{"issue":"2","key":"pdig.0000414.ref005","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1161\/CIRCULATIONAHA.108.795260","article-title":"Derivation and Validation of a Preoperative Prediction Rule for Delirium after Cardiac Surgery","volume":"119","author":"JL Rudolph","year":"2009","journal-title":"Circulation"},{"key":"pdig.0000414.ref006","first-page":"568","article-title":"Delirium Prediction using Machine Learning Models on Preoperative Electronic Health Records Data","volume":"2017","author":"A Davoudi","year":"2017","journal-title":"Proc IEEE Int Symp Bioinforma Bioeng"},{"issue":"3","key":"pdig.0000414.ref007","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1007\/s00268-009-0376-9","article-title":"A Comparison of Three Scores to Screen for Delirium on the Surgical Ward","volume":"34","author":"FM Radtke","year":"2010","journal-title":"World J Surg"},{"issue":"3","key":"pdig.0000414.ref008","doi-asserted-by":"crossref","first-page":"1869","DOI":"10.3390\/ijms24031869","article-title":"Molecular Mechanisms of Neuroinflammation in Aging and Alzheimer\u2019s Disease Progression","volume":"24","author":"FL Andronie-Cioara","year":"2023","journal-title":"Int J Mol Sci"},{"key":"pdig.0000414.ref009","doi-asserted-by":"crossref","first-page":"843069","DOI":"10.3389\/fncel.2022.843069","article-title":"Neuroinflammation as the Underlying Mechanism of Postoperative Cognitive Dysfunction and Therapeutic Strategies","volume":"16","author":"Z Li","year":"2022","journal-title":"Front Cell Neurosci"},{"issue":"4","key":"pdig.0000414.ref010","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1097\/EJA.0000000000000594","article-title":"European Society of Anaesthesiology evidence-based and consensus-based guideline on postoperative delirium","volume":"34","author":"C Aldecoa","year":"2017","journal-title":"Eur J Anaesthesiol"},{"issue":"2","key":"pdig.0000414.ref011","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1097\/EJA.0000000000001876","article-title":"Update of the European Society of Anaesthesiology and Intensive Care Medicine evidence-based and consensus-based guideline on postoperative delirium in adult patients","volume":"41","author":"C Aldecoa","year":"2024","journal-title":"Eur J Anaesthesiol"},{"issue":"4","key":"pdig.0000414.ref012","doi-asserted-by":"crossref","first-page":"58","DOI":"10.5498\/wjp.v2.i4.58","article-title":"Assessment scales for delirium: A review","volume":"2","author":"S Grover","year":"2012","journal-title":"World J Psychiatry"},{"issue":"4","key":"pdig.0000414.ref013","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1093\/bja\/aet167","article-title":"Evaluation of two delirium screening tools for detecting post-operative delirium in the elderly","volume":"111","author":"KJ Neufeld","year":"2013","journal-title":"BJA Br J Anaesth"},{"issue":"8","key":"pdig.0000414.ref014","doi-asserted-by":"crossref","first-page":"2103","DOI":"10.1053\/j.jvca.2020.02.004","article-title":"Predisposing and Precipitating Factors Associated With Postoperative Delirium in Patients Undergoing Cardiac Surgery at a Veterans Affairs Medical Center: A Pilot Retrospective Analysis","volume":"34","author":"MD Russell","year":"2020","journal-title":"J Cardiothorac Vasc Anesth"},{"key":"pdig.0000414.ref015","unstructured":"Evered LA. Predicting delirium: are we there yet?: 3."},{"key":"pdig.0000414.ref016","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.ijnurstu.2017.09.014","article-title":"Development and validation of an automated delirium risk assessment system (Auto-DelRAS) implemented in the electronic health record system","volume":"77","author":"KJ Moon","year":"2018","journal-title":"Int J Nurs Stud"},{"issue":"1","key":"pdig.0000414.ref017","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1186\/s12871-019-0694-x","article-title":"Postoperative delirium in critically ill surgical patients: incidence, risk factors, and predictive scores","volume":"19","author":"O Chaiwat","year":"2019","journal-title":"BMC Anesthesiol"},{"issue":"9","key":"pdig.0000414.ref018","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1002\/jhm.2062","article-title":"The AWOL tool: Derivation and validation of a delirium prediction rule: A Validated Delirium Prediction Rule","volume":"8","author":"VC Douglas","year":"2013","journal-title":"J Hosp Med"},{"issue":"12","key":"pdig.0000414.ref019","doi-asserted-by":"crossref","first-page":"e3072","DOI":"10.1097\/MD.0000000000003072","article-title":"DELirium Prediction Based on Hospital Information (Delphi) in General Surgery Patients","volume":"95","author":"MY Kim","year":"2016","journal-title":"Medicine (Baltimore)"},{"issue":"1","key":"pdig.0000414.ref020","first-page":"7","volume":"110","author":"R Katznelson","year":"2009","journal-title":"Preoperative Use of Statins Is Associated with Reduced Early Delirium Rates after Cardiac Surgery"},{"key":"pdig.0000414.ref021","article-title":"Development and validation of PRE-DELIRIC (PREdiction of DELIRium in ICu patients) delirium prediction model for intensive care patients: observational multicentre study","volume":"344","author":"M van den Boogaard","year":"2012","journal-title":"The BMJ"},{"issue":"3","key":"pdig.0000414.ref022","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s00134-013-3202-7","article-title":"Recalibration of the delirium prediction model for ICU patients (PRE-DELIRIC): a multinational observational study","volume":"40","author":"M van den Boogaard","year":"2014","journal-title":"Intensive Care Med"},{"issue":"6","key":"pdig.0000414.ref023","doi-asserted-by":"crossref","first-page":"1048","DOI":"10.1007\/s00134-015-3777-2","article-title":"Multinational development and validation of an early prediction model for delirium in ICU patients","volume":"41","author":"A Wassenaar","year":"2015","journal-title":"Intensive Care Med"},{"issue":"1","key":"pdig.0000414.ref024","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1186\/s13054-020-2838-2","article-title":"Evaluation of the E-PRE-DELIRIC prediction model for ICU delirium: a retrospective validation in a UK general ICU","volume":"24","author":"SL Cowan","year":"2020","journal-title":"Crit Care"},{"issue":"3","key":"pdig.0000414.ref025","doi-asserted-by":"crossref","first-page":"e212240","DOI":"10.1001\/jamanetworkopen.2021.2240","article-title":"Use of Machine Learning to Develop and Evaluate Models Using Preoperative and Intraoperative Data to Identify Risks of Postoperative Complications","volume":"4","author":"B Xue","year":"2021","journal-title":"JAMA Netw Open"},{"key":"pdig.0000414.ref026","article-title":"Machine Learning to Develop and Internally Validate a Predictive Model for Post-operative Delirium in a Prospective, Observational Clinical Cohort Study of Older Surgical Patients","author":"AM Racine","year":"2020","journal-title":"J Gen Intern Med"},{"issue":"1","key":"pdig.0000414.ref027","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s12871-021-01543-y","article-title":"Postoperative delirium prediction using machine learning models and preoperative electronic health record data","volume":"22","author":"A Bishara","year":"2022","journal-title":"BMC Anesthesiol"},{"issue":"12","key":"pdig.0000414.ref028","doi-asserted-by":"crossref","first-page":"e0296","DOI":"10.1097\/CCE.0000000000000296","article-title":"ICU Delirium-Prediction Models: A Systematic Review","volume":"2","author":"MM Ruppert","year":"2020","journal-title":"Crit Care Explor"},{"key":"pdig.0000414.ref029","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.iccn.2017.01.008","article-title":"Effect of nocturnal sound reduction on the incidence of delirium in intensive care unit patients: An interrupted time series analysis","volume":"41","author":"I van de Pol","year":"2017","journal-title":"Intensive Crit Care Nurs"},{"issue":"2","key":"pdig.0000414.ref030","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1186\/cc7154","article-title":"Clinical review: The impact of noise on patients\u2019 sleep and the effectiveness of noise reduction strategies in intensive care units","volume":"13","author":"H Xie","year":"2009","journal-title":"Crit Care"},{"issue":"4","key":"pdig.0000414.ref031","doi-asserted-by":"crossref","first-page":"e182","DOI":"10.1097\/CCM.0000000000006152","article-title":"Modification in ICU Design May Affect Delirium and Circadian Melatonin: A Proof of Concept Pilot Study","volume":"52","author":"C Spies","year":"2024","journal-title":"Crit Care Med"},{"issue":"1","key":"pdig.0000414.ref032","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1038\/s41746-019-0158-1","article-title":"Why digital medicine depends on interoperability","volume":"2","author":"M Lehne","year":"2019","journal-title":"Npj Digit Med"},{"issue":"2","key":"pdig.0000414.ref033","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/j.jbi.2007.09.003","article-title":"Grand challenges in clinical decision support","volume":"41","author":"DF Sittig","year":"2008","journal-title":"J Biomed Inform"},{"issue":"4","key":"pdig.0000414.ref034","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1109\/TLA.2008.4815284","article-title":"Model-driven reverse engineering for data warehouse design","volume":"6","author":"JN Mazon","year":"2008","journal-title":"IEEE Lat Am Trans"},{"issue":"7","key":"pdig.0000414.ref035","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1007\/s00103-007-0283-x","article-title":"ICD und OPS: Historische Entwicklung und aktueller Stand","volume":"50","author":"B. Graubner","year":"2007","journal-title":"Bundesgesundheitsblatt\u2014Gesundheitsforschung\u2014Gesundheitsschutz"},{"key":"pdig.0000414.ref036","volume-title":"Encyclopedia of Statistics in Behavioral Science","author":"DC Howell","year":"2005","edition":"1"},{"issue":"1","key":"pdig.0000414.ref037","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s41512-020-00077-0","article-title":"Informative missingness in electronic health record systems: the curse of knowing","volume":"4","author":"RHH Groenwold","year":"2020","journal-title":"Diagn Progn Res"},{"issue":"1","key":"pdig.0000414.ref038","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1214\/aoms\/1177730491","article-title":"On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other","volume":"18","author":"HB Mann","year":"1947","journal-title":"Ann Math Stat"},{"issue":"1","key":"pdig.0000414.ref039","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing","volume":"57","author":"Y Benjamini","year":"1995","journal-title":"J R Stat Soc Ser B Methodol"},{"issue":"4","key":"pdig.0000414.ref040","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/S0895-4356(00)00314-0","article-title":"Adjusting for multiple testing\u2014when and how?","volume":"54","author":"R Bender","year":"2001","journal-title":"J Clin Epidemiol"},{"issue":"1","key":"pdig.0000414.ref041","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","article-title":"The meaning and use of the area under a receiver operating characteristic (ROC) curve","volume":"143","author":"JA Hanley","year":"1982","journal-title":"Radiology"},{"issue":"3","key":"pdig.0000414.ref042","first-page":"227","article-title":"Explaining odds ratios","volume":"19","author":"M. Szumilas","year":"2010","journal-title":"J Can Acad Child Adolesc Psychiatry J Acad Can Psychiatr Enfant Adolesc"},{"key":"pdig.0000414.ref043","doi-asserted-by":"crossref","unstructured":"Shekar BH, Dagnew G. Grid Search-Based Hyperparameter Tuning and Classification of Microarray Cancer Data. In: 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP) [Internet]. Gangtok, India: IEEE; 2019 [cited 2024 Apr 18]. p. 1\u20138. https:\/\/ieeexplore.ieee.org\/document\/8882943\/","DOI":"10.1109\/ICACCP.2019.8882943"},{"issue":"4","key":"pdig.0000414.ref044","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1111\/j.1467-9868.2007.00607.x","article-title":"L1-Regularization Path Algorithm for Generalized Linear Models","volume":"69","author":"Mee Young Park","year":"2007","journal-title":"J R Stat Soc Ser B Stat Methodol"},{"issue":"2","key":"pdig.0000414.ref045","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1111\/j.2517-6161.1974.tb00994.x","article-title":"Cross-Validatory Choice and Assessment of Statistical Predictions","volume":"36","author":"M. Stone","year":"1974","journal-title":"J R Stat Soc Ser B Stat Methodol"},{"issue":"17","key":"pdig.0000414.ref046","doi-asserted-by":"crossref","first-page":"3299","DOI":"10.19026\/rjaset.6.3638","article-title":"Standardization and Its Effects on K-Means Clustering Algorithm","volume":"6","author":"IB Mohamad","year":"2013","journal-title":"Res J Appl Sci Eng Technol"},{"issue":"10","key":"pdig.0000414.ref047","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1016\/j.jclinepi.2006.01.014","article-title":"Review: A gentle introduction to imputation of missing values","volume":"59","author":"ART Donders","year":"2006","journal-title":"J Clin Epidemiol"},{"issue":"14\u201315","key":"pdig.0000414.ref048","doi-asserted-by":"crossref","first-page":"2627","DOI":"10.1016\/S1352-2310(97)00447-0","article-title":"Artificial neural networks (the multilayer perceptron)\u2014a review of applications in the atmospheric sciences","volume":"32","author":"MW Gardner","year":"1998","journal-title":"Atmos Environ"},{"issue":"5","key":"pdig.0000414.ref049","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","article-title":"Multilayer feedforward networks are universal approximators","volume":"2","author":"K Hornik","year":"1989","journal-title":"Neural Netw"},{"issue":"2","key":"pdig.0000414.ref050","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"J Bergstra","year":"2012","journal-title":"J Mach Learn Res"},{"key":"pdig.0000414.ref051","doi-asserted-by":"crossref","unstructured":"Lin TY, Goyal P, Girshick R, He K, Dollar P. Focal Loss for Dense Object Detection. In: 2017 IEEE International Conference on Computer Vision (ICCV) [Internet]. Venice: IEEE; 2017 [cited 2024 Apr 18]. p. 2999\u20133007. http:\/\/ieeexplore.ieee.org\/document\/8237586\/","DOI":"10.1109\/ICCV.2017.324"},{"issue":"2","key":"pdig.0000414.ref052","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.1007\/s11063-018-09977-1","article-title":"Learning from Imbalanced Data Sets with Weighted Cross-Entropy Function","volume":"50","author":"YS Aurelio","year":"2019","journal-title":"Neural Process Lett"},{"issue":"4","key":"pdig.0000414.ref053","doi-asserted-by":"crossref","first-page":"e1249","DOI":"10.1002\/widm.1249","article-title":"Ensemble learning: A survey","volume":"8","author":"O Sagi","year":"2018","journal-title":"Wiley Interdiscip Rev Data Min Knowl Discov"},{"key":"pdig.0000414.ref054","doi-asserted-by":"crossref","unstructured":"Tin Kam Ho. Random decision forests. In: Proceedings of 3rd International Conference on Document Analysis and Recognition [Internet]. Montreal, Que., Canada: IEEE Comput. Soc. Press; 1995 [cited 2024 Apr 18]. p. 278\u201382. http:\/\/ieeexplore.ieee.org\/document\/598994\/","DOI":"10.1109\/ICDAR.1995.598994"},{"issue":"4","key":"pdig.0000414.ref055","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1111\/j.1365-2656.2008.01390.x","article-title":"A working guide to boosted regression trees","volume":"77","author":"J Elith","year":"2008","journal-title":"J Anim Ecol"},{"key":"pdig.0000414.ref056","doi-asserted-by":"crossref","unstructured":"Davis J, Goadrich M. The relationship between Precision-Recall and ROC curves. In: Proceedings of the 23rd international conference on Machine learning\u2014ICML \u201806 [Internet]. Pittsburgh, Pennsylvania: ACM Press; 2006 [cited 2024 Apr 18]. p. 233\u201340. http:\/\/portal.acm.org\/citation.cfm?doid=1143844.1143874","DOI":"10.1145\/1143844.1143874"},{"key":"pdig.0000414.ref057","doi-asserted-by":"crossref","unstructured":"Wardhani NWS, Rochayani MY, Iriany A, Sulistyono AD, Lestantyo P. Cross-validation Metrics for Evaluating Classification Performance on Imbalanced Data. In: 2019 International Conference on Computer, Control, Informatics and its Applications (IC3INA) [Internet]. Tangerang, Indonesia: IEEE; 2019 [cited 2024 Apr 18]. p. 14\u20138. https:\/\/ieeexplore.ieee.org\/document\/8949568\/","DOI":"10.1109\/IC3INA48034.2019.8949568"},{"key":"pdig.0000414.ref058","doi-asserted-by":"crossref","unstructured":"Yacouby R, Axman D. Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models. In: Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems [Internet]. Online: Association for Computational Linguistics; 2020 [cited 2024 Apr 18]. p. 79\u201391. https:\/\/www.aclweb.org\/anthology\/2020.eval4nlp-1.9","DOI":"10.18653\/v1\/2020.eval4nlp-1.9"},{"key":"pdig.0000414.ref059","doi-asserted-by":"crossref","unstructured":"Efron B, Tibshirani RJ. An Introduction to the Bootstrap [Internet]. 0 ed. Chapman and Hall\/CRC; 1994 [cited 2024 Apr 18]. https:\/\/www.taylorfrancis.com\/books\/9781000064988","DOI":"10.1201\/9780429246593"},{"key":"pdig.0000414.ref060","unstructured":"Giesa N. https:\/\/github.com\/ngiesa\/icdep [Internet]. GitHub. 2023 [cited 2023 Oct 24]. https:\/\/github.com\/ngiesa\/icdep"},{"key":"pdig.0000414.ref061","unstructured":"Giesa N. Dryad data repository [Internet]. Dryad. 2024 [cited 2024 Apr 18]."},{"issue":"1","key":"pdig.0000414.ref062","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.clsr.2008.11.002","article-title":"Data protection in Germany I: The population census decision and the right to informational self-determination","volume":"25","author":"G Hornung","year":"2009","journal-title":"Comput Law Secur Rev"},{"issue":"7","key":"pdig.0000414.ref063","doi-asserted-by":"crossref","first-page":"e048008","DOI":"10.1136\/bmjopen-2020-048008","article-title":"Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence","volume":"11","author":"GS Collins","year":"2021","journal-title":"BMJ Open"},{"issue":"20","key":"pdig.0000414.ref064","first-page":"1790","volume":"38","author":"SJ Fineberg","year":"2013","journal-title":"Incidence and Risk Factors for Postoperative Delirium After Lumbar Spine Surgery: Spine"},{"issue":"3","key":"pdig.0000414.ref065","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1177\/1089253210374650","article-title":"Predictive Model for Postoperative Delirium in Cardiac Surgical Patients","volume":"14","author":"A Afonso","year":"2010","journal-title":"Semin Cardiothorac Vasc Anesth"},{"issue":"1","key":"pdig.0000414.ref066","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1186\/s12877-020-1449-8","article-title":"Incidence of and risk factors for postoperative delirium in older adult patients undergoing noncardiac surgery: a prospective study","volume":"20","author":"A Iamaroon","year":"2020","journal-title":"BMC Geriatr"},{"key":"pdig.0000414.ref067","doi-asserted-by":"crossref","first-page":"110158","DOI":"10.1016\/j.jclinane.2020.110158","article-title":"Development and validation of a delirium risk prediction preoperative model for cardiac surgery patients (DELIPRECAS): An observational multicentre study","volume":"69","author":"O de la Varga-Mart\u00ednez","year":"2021","journal-title":"J Clin Anesth"},{"issue":"feb09 3","key":"pdig.0000414.ref068","doi-asserted-by":"crossref","first-page":"e420","DOI":"10.1136\/bmj.e420","article-title":"Development and validation of PRE-DELIRIC (PREdiction of DELIRium in ICu patients) delirium prediction model for intensive care patients: observational multicentre study","volume":"344","author":"M v. d Boogaard","year":"2012","journal-title":"BMJ"},{"key":"pdig.0000414.ref069","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.neuroimage.2013.10.067","article-title":"On the interpretation of weight vectors of linear models in multivariate neuroimaging","volume":"87","author":"S Haufe","year":"2014","journal-title":"NeuroImage"},{"key":"pdig.0000414.ref070","unstructured":"Wilming R, Budding C, M\u00fcller KR, Haufe S. Scrutinizing XAI using linear ground-truth data with suppressor variables. 2021 [cited 2023 Oct 24]; https:\/\/arxiv.org\/abs\/2111.07473"},{"key":"pdig.0000414.ref071","unstructured":"MIMIC-III, a freely accessible critical care database | Scientific Data [Internet]. [cited 2021 Dec 5]. https:\/\/www.nature.com\/articles\/sdata201635"},{"key":"pdig.0000414.ref072","doi-asserted-by":"crossref","unstructured":"Wang S, McDermott MBA, Chauhan G, Hughes MC, Naumann T, Ghassemi M. MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III. In: Proceedings of the ACM Conference on Health, Inference, and Learning [Internet]. 2020 [cited 2023 Dec 12]. p. 222\u201335. http:\/\/arxiv.org\/abs\/1907.08322","DOI":"10.1145\/3368555.3384469"}],"container-title":["PLOS Digital Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pdig.0000414","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T17:33:12Z","timestamp":1723656792000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pdig.0000414"}},"subtitle":[],"editor":[{"given":"Nicole Yee-Key","family":"Li-Jessen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2024,8,14]]},"references-count":72,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,8,14]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pdig.0000414","relation":{},"ISSN":["2767-3170"],"issn-type":[{"value":"2767-3170","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,14]]}}}