{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T23:53:03Z","timestamp":1790380383918,"version":"4.1.0"},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Aging Neurosci."],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>With the aging of populations and the high prevalence of stroke, postoperative stroke has become a growing concern. This study aimed to establish a prediction model and assess the risk factors for stroke in elderly patients during the postoperative period.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>ML (Machine learning) prediction models were applied to elderly patients from the MIMIC (Medical Information Mart for Intensive Care)-III and MIMIC-VI databases. The SMOTENC (synthetic minority oversampling technique for nominal and continuous data) balancing technique and iterative SVD (Singular Value Decomposition) data imputation method were used to address the problem of category imbalance and missing values, respectively. We analyzed the possible predictive factors of stroke in elderly patients using seven modeling approaches to train the model. The diagnostic value of the model derived from machine learning was evaluated by the ROC curve (receiver operating characteristic curve).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We analyzed 7,128 and 661 patients from MIMIC-VI and MIMIC-III, respectively. The XGB (extreme gradient boosting) model got the highest AUC (area under the curve) of 0.78 (0.75\u20130.81), making it better than the other six models, Besides, we found that XGB model with databalancing was better than that without data balancing. Based on this prediction model, we found hypertension, cancer, congestive heart failure, chronic pulmonary disease and peripheral vascular disease were the top five predictors. Furthermore, we demonstrated that hypertension predicted postoperative stroke is much more valuable.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Stroke in elderly patients during the postoperative period can be reliably predicted. We proved XGB model is a reliable predictive model, and the history of hypertension should be weighted more heavily than the results of laboratory tests to prevent postoperative stroke in elderly patients regardless of gender.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fnagi.2022.897611","type":"journal-article","created":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T12:09:08Z","timestamp":1658146148000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["Machine Learning Prediction Models for Postoperative Stroke in Elderly Patients: Analyses of the MIMIC Database"],"prefix":"10.3389","volume":"14","author":[{"given":"Xiao","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ningbo","family":"Fei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinxin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zongping","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,7,18]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"562.e","DOI":"10.1016\/j.wneu.2016.09.086","article-title":"Outcomes and Complications After Endovascular Treatment of Brain Arteriovenous Malformations: A Prognostication Attempt Using Artificial Intelligence.","volume":"96","author":"Asadi","year":"2016","journal-title":"World Neurosurg."},{"key":"B2","doi-asserted-by":"publisher","first-page":"e19","DOI":"10.1016\/j.acra.2019.03.015","article-title":"Deep Learning in the Prediction of Ischaemic Stroke Thrombolysis Functional Outcomes: A Pilot Study.","volume":"27","author":"Bacchi","year":"2020","journal-title":"Acad. Radiol."},{"key":"B3","doi-asserted-by":"publisher","first-page":"2222","DOI":"10.1093\/aje\/kwz189","article-title":"What is Machine Learning? A Primer for the Epidemiologist","volume":"188","author":"Bi","year":"2019","journal-title":"Am. J. Epidemiol."},{"key":"B4","doi-asserted-by":"publisher","first-page":"E55","DOI":"10.1503\/cjs.003913","article-title":"Impact of perioperative acute ischemic stroke on the outcomes of noncardiac and nonvascular surgery: a single centre prospective study.","volume":"57","author":"Biteker","year":"2014","journal-title":"Can. J. Surg."},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.2196\/24246","article-title":"A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation.","volume":"23","author":"Bolourani","year":"2021","journal-title":"J. Med. Internet Res."},{"key":"B6","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.soin.2018.06.008","article-title":"[Semantics, epidemiology and semiology of stroke].","volume":"63","author":"Boursin","year":"2018","journal-title":"Soins"},{"key":"B7","doi-asserted-by":"publisher","first-page":"1346","DOI":"10.1097\/aln.0000000000002694","article-title":"Artificial Intelligence and Machine Learning in Anesthesiology.","volume":"131","author":"Connor","year":"2019","journal-title":"Anesthesiology"},{"key":"B8","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1016\/j.jbi.2016.02.012","article-title":"Predictive analysis for identifying potentially undiagnosed post-stroke spasticity patients in United Kingdom.","volume":"60","author":"Cox","year":"2016","journal-title":"J. Biomed. Inform."},{"key":"B9","doi-asserted-by":"publisher","first-page":"1920","DOI":"10.1161\/circulationaha.115.001593","article-title":"Machine Learning in Medicine.","volume":"132","author":"Deo","year":"2015","journal-title":"Circulation"},{"key":"B10","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1093\/ageing\/afn282","article-title":"Human and economic burden of stroke.","volume":"38","author":"Di Carlo","year":"2009","journal-title":"Age Ageing"},{"key":"B11","doi-asserted-by":"publisher","first-page":"3786","DOI":"10.1093\/bioinformatics\/btz134","article-title":"Missing value estimation methods for DNA methylation data.","volume":"35","author":"Di Lena","year":"2019","journal-title":"Bioinformatics"},{"key":"B12","doi-asserted-by":"publisher","first-page":"1635","DOI":"10.1016\/j.jstrokecerebrovasdis.2016.12.017","article-title":"Risk Factors and Stroke Characteristic in Patients with Postoperative Strokes.","volume":"26","author":"Dong","year":"2017","journal-title":"J. Stroke Cerebrovasc. Dis."},{"key":"B13","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1213\/ane.0000000000002157","article-title":"Perioperative Low Arterial Oxygenation Is Associated With Increased Stroke Risk in Cardiac Surgery.","volume":"125","author":"Dunham","year":"2017","journal-title":"Anesth. Analg."},{"key":"B14","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1111\/joim.12822","article-title":"eDoctor: machine learning and the future of medicine.","volume":"284","author":"Handelman","year":"2018","journal-title":"J. Intern. Med."},{"key":"B15","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1161\/strokeaha.118.024293","article-title":"Machine Learning-Based Model for Prediction of Outcomes in Acute Stroke.","volume":"50","author":"Heo","year":"2019","journal-title":"Stroke"},{"key":"B16","doi-asserted-by":"publisher","first-page":"213574","DOI":"10.1109\/access.2020.3040437","article-title":"HealthSOS: Real-Time Health Monitoring System for Stroke Prognostics.","volume":"8","author":"Hussain","year":"2020","journal-title":"IEEE Access"},{"key":"B17","doi-asserted-by":"publisher","first-page":"123146","DOI":"10.1109\/access.2021.3109806","article-title":"Big-ECG: Cardiographic Predictive Cyber-Physical System for Stroke Management.","volume":"9","author":"Hussain","year":"","journal-title":"IEEE Access"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.3390\/s21165334","article-title":"Prediction of Myoelectric Biomarkers in Post-Stroke Gait.","volume":"21","author":"Hussain","year":"","journal-title":"Sensors"},{"key":"B19","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1136\/svn-2017-000101","article-title":"Artificial intelligence in healthcare: past, present and future.","volume":"2","author":"Jiang","year":"2017","journal-title":"Stroke Vasc. Neurol."},{"key":"B20","doi-asserted-by":"publisher","first-page":"2377","DOI":"10.1007\/s00701-016-2966-2","article-title":"Perioperative stroke after carotid endarterectomy: etiology and implications.","volume":"158","author":"Khattar","year":"2016","journal-title":"Acta Neurochir."},{"key":"B21","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1097\/ACO.0b013e328336aeef","article-title":"Outcomes in perioperative care.","volume":"23","author":"Mantz","year":"2010","journal-title":"Curr. Opin. Anaesthesiol."},{"key":"B22","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1177\/0003319712473637","article-title":"Copeptin level after carotid endarterectomy and perioperative stroke.","volume":"65","author":"Maravic-Stojkovic","year":"2014","journal-title":"Angiology"},{"key":"B23","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1212\/cpj.0000000000000082","article-title":"Clinical stroke challenges: A practical approach.","volume":"4","author":"Merino","year":"2014","journal-title":"Neurol. Clin. Pract."},{"key":"B24","doi-asserted-by":"publisher","first-page":"1508","DOI":"10.48550\/arXiv.1407.2330","article-title":"Synthetic Minority Over-sampling TEchnique(SMOTE) for Predicting Software Build Outcomes.","author":"Pears","year":"2014","journal-title":"Comput. Sci."},{"key":"B25","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1161\/01.Str.0000058493.94875.9f","article-title":"Cardiac baroreceptor sensitivity predicts long-term outcome after acute ischemic stroke.","volume":"34","author":"Robinson","year":"2003","journal-title":"Stroke"},{"key":"B26","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1080\/01616412.2019.1609159","article-title":"Predictive analytics and machine learning in stroke and neurovascular medicine.","volume":"41","author":"Saber","year":"2019","journal-title":"Neurol. Res."},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.1016\/j.jstrokecerebrovasdis.2020.105162","article-title":"Machine Learning for Brain Stroke: A Review.","volume":"29","author":"Sirsat","year":"2020","journal-title":"J. Stroke Cerebrovasc. Dis."},{"key":"B28","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1159\/000481578","article-title":"Ischemic Stroke: Histological Thrombus Composition and Pre-Interventional CT Attenuation Are Associated with Intervention Time and Rate of Secondary Embolism.","volume":"44","author":"Sporns","year":"2017","journal-title":"Cerebrovasc. Dis."},{"key":"B29","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1093\/bioinformatics\/17.6.520","article-title":"Missing value estimation methods for DNA microarrays.","volume":"17","author":"Troyanskaya","year":"2001","journal-title":"Bioinformatics"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph17061828","article-title":"Stroke Prediction with Machine Learning Methods among Older Chinese.","volume":"17","author":"Wu","year":"2020","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"B31","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1016\/j.ejso.2020.01.026","article-title":"Incidence and potential predictors of thromboembolic events in epithelial ovarian carcinoma patients during perioperative period.","volume":"46","author":"Zhou","year":"2020","journal-title":"Eur. J. Surg. Oncol."}],"container-title":["Frontiers in Aging Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnagi.2022.897611\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T12:09:12Z","timestamp":1658146152000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnagi.2022.897611\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,18]]},"references-count":31,"alternative-id":["10.3389\/fnagi.2022.897611"],"URL":"https:\/\/doi.org\/10.3389\/fnagi.2022.897611","relation":{},"ISSN":["1663-4365"],"issn-type":[{"value":"1663-4365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,18]]},"article-number":"897611"}}