{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T17:06:20Z","timestamp":1793207180305,"version":"4.5.0"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T00:00:00Z","timestamp":1704931200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82071180"],"award-info":[{"award-number":["82071180"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82271206"],"award-info":[{"award-number":["82271206"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Natural Science Foundation of Beijing","doi-asserted-by":"publisher","award":["7212023"],"award-info":[{"award-number":["7212023"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>In this study, we were aimed to identify important variables via machine learning algorithms and predict postoperative delirium (POD) occurrence in older patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study was to make the secondary analysis of data from a randomized controlled trial. The Boruta function was used to screen relevant basic characteristic variables. Four models including Logistic Regression (LR), K-Nearest Neighbor (KNN), the Classification and Regression Tree (CART), and Random Forest (RF) were established from the data set using repeated cross validation, hyper-parameter optimization, and Smote technique (Synthetic minority over-sampling technique, Smote), with the calculation of confusion matrix parameters and the plotting of Receiver operating characteristic curve (ROC), Precision recall curve (PRC), and partial dependence graph for further analysis and evaluation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The basic characteristic variables resulting from Boruta screening included grouping, preoperative Mini-Mental State Examination(MMSE), CHARLSON score, preoperative HCT, preoperative serum creatinine, intraoperative bleeding volume, intraoperative urine volume, anesthesia duration, operation duration, postoperative morphine dosage, intensive care unit (ICU) duration, tracheal intubation duration, and 7-day postoperative rest and move pain score (median and max; VAS-Rest-M, VAS-Move-M, VAS-Rest-Max, and VAS-Move-Max). And Random Forest (RF) showed the best performance in the testing set among the 4 models with Accuracy: 0.9878; Matthews correlation coefficient (MCC): 0.8763; Area under ROC curve (AUC-ROC): 1.0; Area under the PRC Curve (AUC-PRC): 1.0.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>A high-performance algorithm was established and verified in this study demonstrating the degree of POD risk changes in perioperative elderly patients. And the major risk factors for the development of POD were CREA and VAS-Move-Max.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fneur.2023.1325941","type":"journal-article","created":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T00:27:25Z","timestamp":1704932845000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Random forest algorithm for predicting postoperative delirium in older patients"],"prefix":"10.3389","volume":"14","author":[{"given":"Weixuan","family":"Sheng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianshi","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyun","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengfei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huihui","family":"Miao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongxin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianzuo","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,1,11]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1186\/s13643-020-01518-z","article-title":"Preoperative prognostic factors associated with postoperative delirium in older people undergoing surgery: protocol for a systematic review and individual patient data meta-analysis","volume":"9","author":"Buchan","year":"2020","journal-title":"Syst Rev"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1001\/jamainternmed.2019.4446","article-title":"Effect of the tailored, family-involved hospital elder life program on postoperative delirium and function in older adults: a randomized clinical trial","volume":"180","author":"Wang","year":"2020","journal-title":"JAMA Intern Med"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/s41999-019-00260-7","article-title":"Non-pharmacological approaches in the prevention of delirium","volume":"11","author":"Salvi","year":"2020","journal-title":"Eur Geriatr Med"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1055\/s-0040-1710572","article-title":"Prevention and management of delirium in the intensive care unit","volume":"42","author":"Mart","year":"2021","journal-title":"Semin Respir Crit Care Med"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1097\/ALN.0000000000003834","article-title":"Delirium in older patients after combined epidural-general anesthesia or general anesthesia for major surgery: a randomized trial","volume":"135","author":"Li","year":"2021","journal-title":"Anesthesiology"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"2703","DOI":"10.1001\/jama.286.21.2703","article-title":"Delirium in mechanically ventilated patients: validity and reliability of the confusion assessment method for the intensive care unit (CAM-ICU)","volume":"286","author":"Ely","year":"2001","journal-title":"JAMA"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/j.jcrc.2012.10.004","article-title":"Delirium assessment using confusion assessment method for the intensive care unit in Chinese critically ill patients","volume":"28","author":"Wang","year":"2013","journal-title":"J Crit Care"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1097\/CCM.0b013e3182376e4f","article-title":"Haloperidol prophylaxis decreases delirium incidence in elderly patients after noncardiac surgery: a randomized controlled trial*","volume":"40","author":"Wang","year":"2012","journal-title":"Crit Care Med"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"R238","DOI":"10.1186\/cc9393","article-title":"High serum cortisol level is associated with increased risk of delirium after coronary artery bypass graft surgery: a prospective cohort study","volume":"14","author":"Mu","year":"2010","journal-title":"Crit Care"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"e803","DOI":"10.7717\/peerj-cs.803","article-title":"Utilizing grid search cross-validation with adaptive boosting for augmenting performance of machine learning models","volume":"8","author":"Adnan","year":"2022","journal-title":"PeerJ Comput Sci"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J Artif Intell Res"},{"key":"ref12","author":"Hui","year":"2005"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"m441","DOI":"10.1136\/bmj.m441","article-title":"Calculating the sample size required for developing a clinical prediction model","volume":"368","author":"Riley","year":"2020","journal-title":"BMJ"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"2455","DOI":"10.1177\/0962280218784726","article-title":"Sample size for binary logistic prediction models: beyond events per variable criteria","volume":"28","author":"van Smeden","year":"2019","journal-title":"Stat Methods Med Res"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1002\/sim.8806","article-title":"A note on estimating the cox-Snell R2 from a reported C statistic (AUROC) to inform sample size calculations for developing a prediction model with a binary outcome","volume":"40","author":"Riley","year":"2021","journal-title":"Stat Med"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"81","DOI":"10.2147\/CIA.S395893","article-title":"High ASA physical status and low serum uric acid to creatinine ratio are independent risk factors for postoperative delirium among older adults undergoing urinary calculi surgery[J]","volume":"18","author":"Liu","year":"2023","journal-title":"Clin Interv Aging"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"608","DOI":"10.1111\/cns.13758","article-title":"Automated machine learning-based model predicts postoperative delirium using readily extractable perioperative collected electronic data","volume":"28","author":"Hu","year":"2022","journal-title":"CNS Neurosci Ther"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"992","DOI":"10.1097\/ALN.0000000000004015","article-title":"Spinal anesthesia with targeted sedation based on bispectral index values compared with general anesthesia with masked Bispectral index values to reduce delirium: the SHARP randomized controlled trial","volume":"135","author":"Brown","year":"2021","journal-title":"Anesthesiology"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"777","DOI":"10.3390\/brainsci11060777","article-title":"Anemia and the risk of cognitive impairment: An updated systematic review and meta-analysis","volume":"11","author":"Kung","year":"2021","journal-title":"Brain Sci"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1186\/s13018-020-01651-4","article-title":"Prevalence and risk factors of postoperative delirium after spinal surgery: a meta-analysis","volume":"15","author":"Gao","year":"2020","journal-title":"J Orthop Surg Res"},{"key":"ref21","first-page":"132","article-title":"Application of phenylephrine combined with goal-directed fluid therapy in elderly patients undergoing hip arthroplasty: a randomized controlled trial","volume":"28","author":"Wang","year":"2022","journal-title":"Altern Ther Health Med"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"110791","DOI":"10.1016\/j.exger.2019.110791","article-title":"The potential mechanism of postoperative cognitive dysfunction in older people","volume":"130","author":"Lin","year":"2020","journal-title":"Exp Gerontol"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"487","DOI":"10.3390\/medicina58040487","article-title":"Opioid-sparing analgesia impacts the perioperative anesthetic Management in Major Abdominal Surgery[J]","volume":"58","author":"Jipa","year":"2022","journal-title":"Medicina"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1186\/s13643-022-02156-3","article-title":"Postoperative pain and pain management and neurocognitive outcomes after non-cardiac surgery: a protocol for a series of systematic reviews[J]","volume":"11","author":"Khaled","year":"2022","journal-title":"Syst Rev"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1097\/ACO.0b013e32833020dc","article-title":"Pain management in the elderly and cognitively impaired patient: the role of regional anesthesia and analgesia","volume":"22","author":"Halaszynski","year":"2009","journal-title":"Curr Opin Anaesthesiol"},{"key":"ref26","first-page":"1066","article-title":"Postoperative delirium: risk factors, diagnosis and perioperative care","volume":"79","author":"Bilotta","year":"2013","journal-title":"Minerva Anestesiol"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"106688","DOI":"10.1016\/j.engappai.2023.106688","article-title":"Strategies for enhancing the performance of news article classification in Bangla: handling imbalance and interpretation","volume":"125","author":"Hasib","year":"2023","journal-title":"Eng Appl Artif Intell"},{"key":"ref28","author":"Hasib","year":"2020"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"8394","DOI":"10.1109\/ACCESS.2018.2807121","article-title":"An integrated ensemble learning model for imbalanced fault diagnostics and prognostics","volume":"6","author":"Wu","year":"2018","journal-title":"NJ IEEE Access"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"1568","DOI":"10.1109\/TCSS.2023.3263128","article-title":"Depression detection from social networks data based on machine learning and deep learning techniques: An interrogative survey","volume":"10","author":"Hasib","year":"2023","journal-title":"IEEE Trans Comput Soc Syst"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"820","DOI":"10.3389\/fgene.2020.00820","article-title":"Identification of orphan genes in unbalanced datasets based on ensemble learning","volume":"11","author":"Gao","year":"2020","journal-title":"Front Genet"},{"key":"ref32","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/j.cjca.2021.09.004","article-title":"Opening the black box: the promise and limitations of explainable machine learning in cardiology","volume":"38","author":"Petch","year":"2022","journal-title":"Can J Cardiol"},{"key":"ref33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v036.i11","article-title":"Feature selection with the Boruta package","volume":"36","author":"Kursa","year":"2010","journal-title":"J Stat Softw"},{"key":"ref34","doi-asserted-by":"publisher","first-page":"111","DOI":"10.3390\/diagnostics13010111","article-title":"Interpretable machine learning techniques in ECG-based heart disease classification: a systematic review","volume":"13","author":"Ayano","year":"2022","journal-title":"Diagnostics"}],"container-title":["Frontiers in Neurology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fneur.2023.1325941\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T00:27:30Z","timestamp":1704932850000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fneur.2023.1325941\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,11]]},"references-count":34,"alternative-id":["10.3389\/fneur.2023.1325941"],"URL":"https:\/\/doi.org\/10.3389\/fneur.2023.1325941","relation":{},"ISSN":["1664-2295"],"issn-type":[{"value":"1664-2295","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,11]]},"article-number":"1325941"}}