{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T22:25:25Z","timestamp":1793831125900,"version":"4.6.1"},"reference-count":75,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T00:00:00Z","timestamp":1666224000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T00:00:00Z","timestamp":1666224000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Kementerian Sains, Teknologi dan Inovasi,Malaysia","award":["TDF03211036"],"award-info":[{"award-number":["TDF03211036"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Limited research has been conducted in\u00a0Asian elderly patients (aged 65\u00a0years and above) for in-hospital mortality prediction after an ST-segment elevation myocardial infarction (STEMI) using Deep Learning (DL) and Machine Learning (ML). We used DL and ML to predict in-hospital mortality in Asian elderly STEMI patients and compared it to a conventional risk score for myocardial infraction\u00a0outcomes. Malaysia's National Cardiovascular Disease Registry comprises an ethnically diverse Asian elderly population (3991 patients). 50 variables helped in establishing the in-hospital death prediction model. The TIMI score was used to predict mortality using DL and feature selection methods from ML algorithms. The main performance metric was the area under the receiver operating characteristic curve (AUC). The DL and ML model constructed using ML feature selection outperforms\u00a0the conventional risk scoring score, TIMI (AUC 0.75). DL built from ML features (AUC ranging from\u00a00.93 to 0.95) outscored DL built from all features (AUC 0.93). The TIMI score underestimates mortality in the elderly. TIMI predicts 18.4% higher mortality than the DL algorithm (44.7%). All ML feature selection algorithms identify age, fasting blood glucose, heart rate, Killip class, oral hypoglycemic agent, systolic blood pressure, and total cholesterol as common predictors of mortality in the elderly. In a multi-ethnic population, DL outperformed the TIMI risk score in classifying elderly STEMI patients. ML improves death prediction by identifying separate characteristics in older Asian populations. Continuous testing and validation will improve future risk classification, management, and results.<\/jats:p>","DOI":"10.1038\/s41598-022-18839-9","type":"journal-article","created":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T16:04:43Z","timestamp":1666281883000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["In-hospital risk stratification algorithm of Asian elderly patients"],"prefix":"10.1038","volume":"12","author":[{"given":"Sazzli","family":"Kasim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sorayya","family":"Malek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Song","family":"Cheen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Shahreeza","family":"Safiruz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wan Azman Wan","family":"Ahmad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khairul Shafiq","family":"Ibrahim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Firdaus","family":"Aziz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kazuaki","family":"Negishi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nurulain","family":"Ibrahim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,20]]},"reference":[{"key":"18839_CR1","unstructured":"World Health Organization. Media centre: the top 10 causes of death. (2020) (Accessed 20 Nov 2020) https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/the-top-10-causes-of-death."},{"issue":"8","key":"18839_CR2","doi-asserted-by":"publisher","first-page":"1479","DOI":"10.1016\/j.jacc.2005.05.084","volume":"46","author":"KP Alexander","year":"2005","unstructured":"Alexander, K. P., CRUSADE investigators. Evolution in cardiovascular care for elderly patients with non-ST-segment elevation acute coronary syndromes: Results from the CRUSADE national quality improvement initiative. J. Am. Coll. Cardiol. 46(8), 1479\u20131487 (2005).","journal-title":"J. Am. Coll. Cardiol."},{"issue":"3","key":"18839_CR3","doi-asserted-by":"publisher","first-page":"736","DOI":"10.1016\/S0735-1097(01)01432-2","volume":"38","author":"RH Mehta","year":"2001","unstructured":"Mehta, R. H. et al. Acute myocardial infarction in the elderly: Differences by age. J. Am. Coll. Cardiol. 38(3), 736\u2013741 (2001).","journal-title":"J. Am. Coll. Cardiol."},{"issue":"7","key":"18839_CR4","first-page":"10","volume":"3","author":"PH Su","year":"2019","unstructured":"Su, P. H. et al. Comparison of clinical presentations and outcomes between adult and elderly acute myocardial infarction patients in emergency department. Heal Technol. 3(7), 10\u201321037 (2019).","journal-title":"Heal Technol."},{"issue":"19","key":"18839_CR5","doi-asserted-by":"publisher","first-page":"2570","DOI":"10.1161\/CIRCULATIONAHA.107.182616","volume":"115","author":"K Alexander","year":"2007","unstructured":"Alexander, K. et al. Acute coronary care in the elderly, part II: ST-segment\u2013elevation myocardial infarction: A scientific statement for healthcare professionals from the American Heart Association Council on Clinical Cardiology: In collaboration with the Society of Geriatric. Circulation 115(19), 2570\u20132589 (2007).","journal-title":"Circulation"},{"key":"18839_CR6","doi-asserted-by":"publisher","first-page":"3","DOI":"10.47102\/annals-acadmedsg.19110","volume":"49","author":"JX Cai","year":"2020","unstructured":"Cai, J. X. et al. Elderly Asian patients have lower revascularisation rates and poorer outcomes for ST-elevation myocardial infarction compared to younger patients. Ann. Acad. Med. Singapore. 49, 3\u201314 (2020).","journal-title":"Ann. Acad. Med. Singapore."},{"key":"18839_CR7","doi-asserted-by":"publisher","first-page":"60","DOI":"10.2174\/1874192401206010060","volume":"6","author":"E Ahmed","year":"2012","unstructured":"Ahmed, E. et al. Effect of age on clinical presentation and outcome of patients hospitalized with acute coronary syndrome: A 20-year registry in a Middle Eastern Country. Open Cardiovasc. Med. J. 6, 60\u201367 (2012).","journal-title":"Open Cardiovasc. Med. J."},{"key":"18839_CR8","doi-asserted-by":"publisher","first-page":"1551","DOI":"10.1093\/eurheartj\/ehu039","volume":"35","author":"M Zaman","year":"2014","unstructured":"Zaman, M. et al. The association between older age and receipt of care and outcomes in patients with acute coronary syndromes: A cohort study of the Myocardial Ischaemia National Audit Project (MINAP). Eur. Heart J. 35, 1551\u20131558 (2014).","journal-title":"Eur. Heart J."},{"issue":"1","key":"18839_CR9","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.ahj.2004.06.003","volume":"149","author":"A Avezum","year":"2005","unstructured":"Avezum, A. et al. Impact of age on management and outcome of acute coronary syndrome: Observations from the Global Registry of Acute Coronary Events (GRACE). Am. Heart J. 149(1), 67\u201373 (2005).","journal-title":"Am. Heart J."},{"issue":"6","key":"18839_CR10","doi-asserted-by":"publisher","first-page":"714","DOI":"10.1001\/jamacardio.2020.0359","volume":"5","author":"AS Tahhan","year":"2020","unstructured":"Tahhan, A. S. et al. Enrollment of older patients, women, and racial\/ethnic minority groups in contemporary acute coronary syndrome clinical trials: A systematic review. JAMA Cardiol. 5(6), 714\u2013722 (2020).","journal-title":"JAMA Cardiol."},{"issue":"17","key":"18839_CR11","doi-asserted-by":"publisher","first-page":"2031","DOI":"10.1161\/01.CIR.102.17.2031","volume":"102","author":"DA Morrow","year":"2000","unstructured":"Morrow, D. A. et al. TIMI risk score for ST-elevation myocardial infarction: A convenient, bedside, clinical score for risk assessment at presentation: An intravenous nPA for treatment of infarcting myocardium early II trial substudy. Circulation 102(17), 2031\u20132037 (2000).","journal-title":"Circulation"},{"issue":"19","key":"18839_CR12","doi-asserted-by":"publisher","first-page":"2345","DOI":"10.1001\/archinte.163.19.2345","volume":"163","author":"C Granger","year":"2003","unstructured":"Granger, C. et al. Predictors of hospital mortality in the global registry of acute coronary events. Arch. Intern. Med. 163(19), 2345\u20132353 (2003).","journal-title":"Arch. Intern. Med."},{"issue":"10","key":"18839_CR13","doi-asserted-by":"publisher","first-page":"e0224502","DOI":"10.1371\/journal.pone.0224502","volume":"14","author":"JM Kwon","year":"2019","unstructured":"Kwon, J. M. et al. Deep-learning-based risk stratification for mortality of patients with acute myocardial infarction. PLoS ONE 14(10), e0224502 (2019).","journal-title":"PLoS ONE"},{"key":"18839_CR14","first-page":"476","volume":"245","author":"X Li","year":"2017","unstructured":"Li, X. et al. Using machine learning models to predict in-hospital mortality for ST-elevation myocardial infarction patients. Stud. Health Technol. Inform. 245, 476\u2013480 (2017).","journal-title":"Stud. Health Technol. Inform."},{"key":"18839_CR15","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/j.ijcard.2017.05.067","volume":"246","author":"R Shouval","year":"2017","unstructured":"Shouval, R. et al. Machine learning for prediction of 30-day mortality after ST elevation myocardial infraction: An Acute Coronary Syndrome Israeli Survey data mining study. Int. J. Cardiol. 246, 7\u201313 (2017).","journal-title":"Int. J. Cardiol."},{"issue":"1","key":"18839_CR16","first-page":"1","volume":"6","author":"L Yang","year":"2019","unstructured":"Yang, L. Artificial intelligence: A survey on evolution, models, applications and future trends. J. Manag. Anal. 6(1), 1\u201329 (2019).","journal-title":"J. Manag. Anal."},{"key":"18839_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2147\/TCRM.S236498","volume":"16","author":"YM Li","year":"2020","unstructured":"Li, Y. M. et al. Machine learning to predict the 1-year mortality rate after acute anterior myocardial infarction in Chinese patients. Ther. Clin. Risk Manag. 16, 1 (2020).","journal-title":"Ther. Clin. Risk Manag."},{"issue":"8","key":"18839_CR18","doi-asserted-by":"publisher","first-page":"e0254894","DOI":"10.1371\/journal.pone.0254894","volume":"16","author":"F Aziz","year":"2021","unstructured":"Aziz, F. et al. Short-and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach. PLoS ONE 16(8), e0254894 (2021).","journal-title":"PLoS ONE"},{"issue":"6","key":"18839_CR19","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1016\/j.ygeno.2012.04.003","volume":"99","author":"X Chen","year":"2012","unstructured":"Chen, X. & Ishwaran, H. Random forests for genomic data analysis. Genomics 99(6), 323\u2013329 (2012).","journal-title":"Genomics"},{"issue":"1","key":"18839_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-017-0500-y","volume":"17","author":"J Wallert","year":"2017","unstructured":"Wallert, J., Tomasoni, M., Madison, G. & Held, C. Predicting two-year survival versus non-survival after first myocardial infarction using machine learning and Swedish national register data. BMC Med. Inform. Decis. Making. 17(1), 1\u201311 (2017).","journal-title":"BMC Med. Inform. Decis. Making."},{"issue":"3","key":"18839_CR21","first-page":"81","volume":"6","author":"WAW Ahmad","year":"2011","unstructured":"Ahmad, W. A. W. et al. Malaysian national cardiovascular disease database (NCVD)\u2013acute coronary syndrome (ACS) registry: How are we different?. CVD Prev. Control 6(3), 81\u201389 (2011).","journal-title":"CVD Prev. Control"},{"issue":"1","key":"18839_CR22","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.ijcard.2011.08.015","volume":"165","author":"WAW Ahmad","year":"2013","unstructured":"Ahmad, W. A. W. et al. The Journey of Malaysian NCVD\u2014PCI (National Cardiovascular Disease Database\u2014Percutaneous Coronary Intervention) Registry: A summary of three years report. Int. J. Cardiol. 165(1), 161\u2013164 (2013).","journal-title":"Int. J. Cardiol."},{"key":"18839_CR23","unstructured":"Wan Ahmad, W. Annual Report of the NCVD-ACS Registry, 2016\u20132017. (National Cardiovascular Disease Database, 2019)."},{"issue":"7","key":"18839_CR24","doi-asserted-by":"publisher","first-page":"1077","DOI":"10.1016\/j.amjcard.2017.06.044","volume":"120","author":"Y Peng","year":"2017","unstructured":"Peng, Y. et al. Predicting in-hospital mortality in patients with acute coronary syndrome in China. Am. J. Cardiol. 120(7), 1077\u20131083 (2017).","journal-title":"Am. J. Cardiol."},{"key":"18839_CR25","first-page":"1","volume":"45","author":"S Van Buuren","year":"2010","unstructured":"Van Buuren, S. & Groothuis-Oudshoorn, K. mice: Multivariate imputation by chained equations in R. J. Stat. Softw. 45, 1\u201368 (2010).","journal-title":"J. Stat. Softw."},{"key":"18839_CR26","doi-asserted-by":"crossref","unstructured":"Kuhn, M. & Johnson, K. Classification trees and rule-based models. In Applied Predictive Modeling, 369\u2013413 (Springer, 2013).","DOI":"10.1007\/978-1-4614-6849-3_14"},{"key":"18839_CR27","unstructured":"Draelos R. Best Use of Train\/Val\/Test Splits, with Tips for Medical Data. [Online] (2022) https:\/\/glassboxmedicine.com\/2019\/09\/15\/best-use-of-train-val-test-splits-with-tips-for-medical-data."},{"issue":"14","key":"18839_CR28","doi-asserted-by":"publisher","first-page":"2225","DOI":"10.1016\/j.patrec.2010.03.014","volume":"31","author":"R Genuer","year":"2010","unstructured":"Genuer, R., Poggi, J. M. & Tuleau-Malot, C. Variable selection using random forests. Pattern Recogn. Lett. 31(14), 2225\u20132236 (2010).","journal-title":"Pattern Recogn. Lett."},{"issue":"1","key":"18839_CR29","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/BF00993106","volume":"13","author":"C Schaffer","year":"1993","unstructured":"Schaffer, C. Selecting a classification method by cross-validation. Mach. Learn. 13(1), 135\u2013143 (1993).","journal-title":"Mach. Learn."},{"key":"18839_CR30","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.ijcard.2019.11.049","volume":"297","author":"F Aziz","year":"2019","unstructured":"Aziz, F., Malek, S., Ibrahim, K. K. S. & Kasim, S. A novel local machine learning algorithm to predict death in ACS patients. Int. J. Cardiol. 297, 18 (2019).","journal-title":"Int. J. Cardiol."},{"issue":"3","key":"18839_CR31","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","volume":"20","author":"V Vapnik","year":"1995","unstructured":"Vapnik, V., Guyon, I. & Hastie, T. Support vector machines. Mach. Learn. 20(3), 273\u2013297 (1995).","journal-title":"Mach. Learn."},{"key":"18839_CR32","doi-asserted-by":"crossref","unstructured":"Chen, T. & Guestrin, C. Xgboost: a scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785\u2013794 (2016).","DOI":"10.1145\/2939672.2939785"},{"key":"18839_CR33","volume-title":"Pattern Recognition: Statistical, Structural and Neural Approaches","author":"R Schalkoff","year":"1992","unstructured":"Schalkoff, R. Pattern Recognition: Statistical, Structural and Neural Approaches (Wiley, 1992)."},{"key":"18839_CR34","unstructured":"Ioffe, S. & Szegedy, C. Batch normalization: accelerating deep network training by reducing internal covariate shift. In International Conference on Machine Learning 448\u2013456 (2015)."},{"key":"18839_CR35","first-page":"1","volume":"27","author":"C Tang","year":"2014","unstructured":"Tang, C., Srivastava, N. & Salakhutdinov, R. R. Learning generative models with visual attention. Adv. Neural Inf. Process. Syst. 27, 1\u20139 (2014).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"18839_CR36","unstructured":"Kingma, D. P. & Ba, J. Adam: A Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"18839_CR37","volume-title":"Rectified Linear Units Improve Restricted Boltzmann Machines","author":"V Nair","year":"2010","unstructured":"Nair, V. & Hinton, G. E. Rectified Linear Units Improve Restricted Boltzmann Machines (Icml, 2010)."},{"key":"18839_CR38","doi-asserted-by":"publisher","first-page":"89","DOI":"10.7763\/IJCTE.2011.V3.288","volume":"3","author":"T Jayalakshmi","year":"2011","unstructured":"Jayalakshmi, T. & Santhakumaran, A. Statistical normalization and backpropagation for classification. Int. J. Comput. Theory Eng. 3, 89\u201393 (2011).","journal-title":"Int. J. Comput. Theory Eng."},{"issue":"11","key":"18839_CR39","doi-asserted-by":"publisher","first-page":"e113677","DOI":"10.1371\/journal.pone.0113677","volume":"9","author":"D Dunkler","year":"2014","unstructured":"Dunkler, D., Plischke, M., Leffondr\u00e9, K. & Heinze, G. Augmented backward elimination: A pragmatic and purposeful way to develop statistical models. PLoS ONE 9(11), e113677 (2014).","journal-title":"PLoS ONE"},{"issue":"3","key":"18839_CR40","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.eij.2018.03.002","volume":"19","author":"D Jain","year":"2018","unstructured":"Jain, D. & Singh, V. Feature selection and classification systems for chronic disease prediction: A review. Egypt. Inform. J. 19(3), 179\u2013189 (2018).","journal-title":"Egypt. Inform. J."},{"issue":"1","key":"18839_CR41","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1097\/EDE.0b013e3181c30fb2","volume":"21","author":"E Steyerberg","year":"2010","unstructured":"Steyerberg, E. et al. Assessing the performance of prediction models: A framework for traditional and novel measures. Epidemiology 21(1), 128\u2013138 (2010).","journal-title":"Epidemiology"},{"issue":"10","key":"18839_CR42","doi-asserted-by":"publisher","first-page":"e146","DOI":"10.1161\/CIR.0000000000000485","volume":"135","author":"EJ Benjamin","year":"2017","unstructured":"Benjamin, E. J. et al. Heart disease and stroke statistics\u20142017 update: A report from the American Heart Association. Circulation 135(10), e146\u2013e603 (2017).","journal-title":"Circulation"},{"key":"18839_CR43","first-page":"98","volume":"103","author":"LC Correia","year":"2014","unstructured":"Correia, L. C. et al. Prognostic value of TIMI score versus GRACE score in ST-segment elevation myocardial infarction. Arq. Bras. Cardiol. 103, 98\u2013106 (2014).","journal-title":"Arq. Bras. Cardiol."},{"key":"18839_CR44","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L. Random forests. Mach. Learn. 45, 5\u201332 (2001).","journal-title":"Mach. Learn."},{"issue":"6","key":"18839_CR45","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.1109\/TKDE.2003.1245283","volume":"15","author":"MA Hall","year":"2003","unstructured":"Hall, M. A. & Holmes, G. Benchmarking attribute selection techniques for discrete class data mining. IEEE Trans. Knowl. Data Eng. 15(6), 1437\u20131447 (2003).","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"20","key":"18839_CR46","doi-asserted-by":"publisher","first-page":"1620","DOI":"10.1136\/heartjnl-2013-305416","volume":"100","author":"C Labos","year":"2014","unstructured":"Labos, C. et al. Traditional risk factors and a Genetic Risk Score are associated with age of first acute coronary syndrome. Heart 100(20), 1620\u20131624 (2014).","journal-title":"Heart"},{"issue":"3","key":"18839_CR47","doi-asserted-by":"publisher","first-page":"753","DOI":"10.17576\/jsm-2021-5003-17","volume":"50","author":"N Aziida","year":"2021","unstructured":"Aziida, N., Malek, S., Aziz, F., Ibrahim, K. S. & Kasim, S. Predicting 30-day mortality after an acute coronary syndrome (ACS) using machine learning methods for feature selection, classification and visualisation. Sains Malaysiana. 50(3), 753\u2013768 (2021).","journal-title":"Sains Malaysiana."},{"issue":"Supplement_2","key":"18839_CR48","doi-asserted-by":"publisher","first-page":"ehaa946-3494","DOI":"10.1093\/ehjci\/ehaa946.3494","volume":"41","author":"SS Kasim","year":"2020","unstructured":"Kasim, S. S., Malek, S., Ibrahim, K. K. & Aziz, M. F. Risk stratification of Asian patients after ST-elevation myocardial infarction using machine learning methods. Eur. Heart J. 41(Supplement_2), ehaa946-3494 (2020).","journal-title":"Eur. Heart J."},{"issue":"9","key":"18839_CR49","doi-asserted-by":"publisher","first-page":"1270","DOI":"10.1016\/j.amjcard.2013.01.271","volume":"111","author":"S Selvarajah","year":"2013","unstructured":"Selvarajah, S. et al. Impact of cardiac care variation on ST-elevation myocardial infarction outcomes in Malaysia. Am. J. Cardiol. 111(9), 1270\u20131276 (2013).","journal-title":"Am. J. Cardiol."},{"issue":"7","key":"18839_CR50","doi-asserted-by":"publisher","first-page":"e0220289","DOI":"10.1371\/journal.pone.0220289","volume":"14","author":"S Furnaz","year":"2019","unstructured":"Furnaz, S. et al. Performance of the TIMI risk score in predicting mortality after primary percutaneous coronary intervention in elderly women: Results from a developing country. PLoS ONE 14(7), e0220289 (2019).","journal-title":"PLoS ONE"},{"issue":"1","key":"18839_CR51","first-page":"4","volume":"34","author":"YH Chen","year":"2018","unstructured":"Chen, Y. H., Huang, S. S. & Lin, S. J. TIMI and GRACE risk scores predict both short-term and long-term outcomes in Chinese patients with acute myocardial infarction. Acta Cardiol. Sin. 34(1), 4\u201312 (2018).","journal-title":"Acta Cardiol. Sin."},{"issue":"5","key":"18839_CR52","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1016\/j.hrtlng.2015.05.005","volume":"44","author":"SL Feder","year":"2015","unstructured":"Feder, S. L. et al. Physicians\u2019 perceptions of the thrombolysis in myocardial infarction (TIMI) risk score in older adults with acute myocardial infarction. Heart Lung 44(5), 371\u2013381 (2015).","journal-title":"Heart Lung"},{"issue":"4","key":"18839_CR53","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.ahj.2012.12.020","volume":"165","author":"B Bawamia","year":"2013","unstructured":"Bawamia, B., Mehran, R., Qiu, W. L. & Vijay, K. Risk scores in acute coronary syndrome and percutaneous coronary intervention: A review. Am. Heart J. 165(4), 441\u2013450 (2013).","journal-title":"Am. Heart J."},{"key":"18839_CR54","doi-asserted-by":"publisher","first-page":"82390","DOI":"10.1109\/ACCESS.2019.2924353","volume":"7","author":"A Ntakaris","year":"2019","unstructured":"Ntakaris, A., Mirone, G., Kanniainen, J. & Iosifidis, A. Feature engineering for mid-price prediction with deep learning. IEEE Access 7, 82390\u201382412 (2019).","journal-title":"IEEE Access"},{"issue":"3","key":"18839_CR55","doi-asserted-by":"publisher","first-page":"2539","DOI":"10.2174\/1574893612666170125124538","volume":"13","author":"L Yu","year":"2018","unstructured":"Yu, L., Sun, X., Tian, S. & Shi, X. Drug and nondrug classification based on deep learning with various feature selection strategies. Curr. Bioinform. 13(3), 2539 (2018).","journal-title":"Curr. Bioinform."},{"issue":"2","key":"18839_CR56","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1177\/2048872615568966","volume":"5","author":"JM Cheng","year":"2016","unstructured":"Cheng, J. M. et al. A simple risk chart for initial risk assessment of 30-day mortality in patients with cardiogenic shock from ST-elevation myocardial infarction. Eur. Heart J. Acute Cardiovasc. Care. 5(2), 101\u2013107 (2016).","journal-title":"Eur. Heart J. Acute Cardiovasc. Care."},{"issue":"4","key":"18839_CR57","doi-asserted-by":"publisher","first-page":"191","DOI":"10.11622\/smedj.2015145","volume":"57","author":"ASM Zuhdi","year":"2016","unstructured":"Zuhdi, A. S. M. et al. Acute coronary syndrome in the elderly: The Malaysian National Cardiovascular Disease Database-Acute Coronary Syndrome registry. Singap. Med. J. 57(4), 191 (2016).","journal-title":"Singap. Med. J."},{"issue":"9","key":"18839_CR58","doi-asserted-by":"publisher","first-page":"1035","DOI":"10.1016\/S0002-9149(01)01457-6","volume":"87","author":"V DeGeare","year":"2001","unstructured":"DeGeare, V., Boura, J., Grines, L., O\u2019Neill, W. & Grines, C. Predictive value of the Killip classification in patients undergoing primary percutaneous coronary intervention for acute myocardial infarction. Am. J. Cardiol. 87(9), 1035\u20131038 (2001).","journal-title":"Am. J. Cardiol."},{"key":"18839_CR59","doi-asserted-by":"publisher","first-page":"1539","DOI":"10.2147\/CIA.S53305","volume":"8","author":"P Erceg","year":"2013","unstructured":"Erceg, P. et al. Health-related quality of life in elderly patients hospitalized with chronic heart failure. Clin. Interv. Aging 8, 1539 (2013).","journal-title":"Clin. Interv. Aging"},{"issue":"1","key":"18839_CR60","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12933-020-01021-8","volume":"19","author":"JH Lee","year":"2020","unstructured":"Lee, J. H., Han, K. & Huh, J. H. The sweet spot: fasting glucose, cardiovascular disease, and mortality in older adults with diabetes: A nationwide population-based study. Cardiovasc. Diabetol. 19(1), 1\u201310 (2020).","journal-title":"Cardiovasc. Diabetol."},{"issue":"1","key":"18839_CR61","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12933-018-0694-z","volume":"17","author":"G Lee","year":"2018","unstructured":"Lee, G. et al. The effect of change in fasting glucose on the risk of myocardial infarction, stroke, and all-cause mortality: A nationwide cohort study. Cardiovasc. Diabetol. 17(1), 1\u201310 (2018).","journal-title":"Cardiovasc. Diabetol."},{"key":"18839_CR62","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.ahj.2017.06.005","volume":"191","author":"J Wei","year":"2017","unstructured":"Wei, J. et al. Sex-based differences in quality of care and outcomes in a health system using a standardized STEMI protocol. Am. Heart J. 191, 30\u201336 (2017).","journal-title":"Am. Heart J."},{"issue":"4","key":"18839_CR63","doi-asserted-by":"publisher","first-page":"388","DOI":"10.2174\/1570161116666180315104820","volume":"17","author":"A Shehab","year":"2019","unstructured":"Shehab, A. et al. Clinical presentation, quality of care, risk factors and outcomes in women with acute ST-elevation myocardial infarction (STEMI): An observational report from six middle Eastern countries. Curr. Vasc. Pharmacol. 17(4), 388\u2013395 (2019).","journal-title":"Curr. Vasc. Pharmacol."},{"issue":"4","key":"18839_CR64","first-page":"356","volume":"14","author":"M Zachura","year":"2018","unstructured":"Zachura, M. et al. Gender-related differences in men and women with ST-segment elevation myocardial infarction and incomplete infarct-related artery flow restoration: A multicenter national registry. Adv. Interv. Cardiol. 14(4), 356 (2018).","journal-title":"Adv. Interv. Cardiol."},{"key":"18839_CR65","doi-asserted-by":"publisher","first-page":"2215","DOI":"10.1016\/S0140-6736(10)60484-9","volume":"375","author":"N Sarwar","year":"2010","unstructured":"Sarwar, N. et al. Emerging Risk Factors Collaboration Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: A collaborative meta-analysis of 102 prospective studies. Lancet 375, 2215\u20132222 (2010).","journal-title":"Lancet"},{"key":"18839_CR66","doi-asserted-by":"publisher","first-page":"64","DOI":"10.3389\/fcvm.2021.644189","volume":"8","author":"J Cui","year":"2021","unstructured":"Cui, J., Liu, Y., Li, Y., Xu, F. & Liu, Y. Type 2 diabetes and myocardial infarction: Recent clinical evidence and perspective. Front. Cardiovasc. Med. 8, 64 (2021).","journal-title":"Front. Cardiovasc. Med."},{"issue":"12","key":"18839_CR67","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1007\/s40266-018-0598-3","volume":"35","author":"AD Mooradian","year":"2018","unstructured":"Mooradian, A. D. Evidence-based management of diabetes in older adults. Drugs Aging 35(12), 1065\u20131078 (2018).","journal-title":"Drugs Aging"},{"issue":"3","key":"18839_CR68","doi-asserted-by":"publisher","first-page":"e014477","DOI":"10.1161\/JAHA.119.014477","volume":"9","author":"S Kumar","year":"2020","unstructured":"Kumar, S., McDaniel, M., Samady, H. & Forouzandeh, F. Contemporary revascularization dilemmas in older adults. J. Am. Heart Assoc. 9(3), e014477 (2020).","journal-title":"J. Am. Heart Assoc."},{"issue":"18","key":"18839_CR69","doi-asserted-by":"publisher","first-page":"3588","DOI":"10.1080\/00949655.2018.1530773","volume":"88","author":"N Solaro","year":"2018","unstructured":"Solaro, N., Barbiero, A., Manzi, G. & Ferrari, P. A simulation comparison of imputation methods for quantitative data in the presence of multiple data patterns. J. Stat. Comput. Simul. 88(18), 3588\u201335619 (2018).","journal-title":"J. Stat. Comput. Simul."},{"issue":"1","key":"18839_CR70","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1109\/TSMCB.2002.804363","volume":"34","author":"K Mao","year":"2004","unstructured":"Mao, K. Orthogonal forward selection and backward elimination algorithms for feature subset selection. IEEE Trans. Syst. Man Cybern. Part B 34(1), 629\u2013634 (2004).","journal-title":"IEEE Trans. Syst. Man Cybern. Part B"},{"issue":"11","key":"18839_CR71","doi-asserted-by":"publisher","first-page":"e7947","DOI":"10.1371\/journal.pone.0007947","volume":"4","author":"KG Aragam","year":"2009","unstructured":"Aragam, K. G. et al. Does simplicity compromise accuracy in ACS risk prediction? A retrospective analysis of the TIMI and GRACE risk scores. PLoS ONE 4(11), e7947 (2009).","journal-title":"PLoS ONE"},{"issue":"7","key":"18839_CR72","doi-asserted-by":"publisher","first-page":"e008100","DOI":"10.1161\/JAHA.117.008100","volume":"7","author":"G Zhao","year":"2018","unstructured":"Zhao, G. et al..  In\u2010Hospital Outcomes of Dual Loading Antiplatelet Therapy in Patients 75 Years and Older With Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention: Findings From the CCC\u2010ACS (Improving Care for Cardiovascular Disease in China\u2010Acute Coronary Syndrome) Project.\u00a0J. Am. Heart Assoc.\u00a07(7), e008100 (2018).","journal-title":"J. Am. Heart Assoc."},{"key":"18839_CR73","unstructured":"Khaire, U. M. & Dhanalakshmi, R. Stability of feature selection algorithm: A review. J. King Saud Univ.-Comput. Inf. Sci. (2019)."},{"key":"18839_CR74","doi-asserted-by":"crossref","unstructured":"Hu, G. et al. Smoking and provision of smoking cessation interventions among inpatients with acute coronary syndrome in China: Findings from the Improving Care for Cardiovascular Disease in China-Acute Coronary Syndrome Project. Glob. Heart 15(1) (2020).","DOI":"10.5334\/gh.784"},{"issue":"2","key":"18839_CR75","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1080\/03007995.2018.1491149","volume":"35","author":"S Irawati","year":"2019","unstructured":"Irawati, S. et al. Long-term incidence and risk factors of cardiovascular events in Asian populations: Systematic review and meta-analysis of population-based cohort studies. Curr. Med. Res. Opin. 35(2), 291\u2013299 (2019).","journal-title":"Curr. Med. Res. Opin."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-022-18839-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-022-18839-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-022-18839-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T01:06:34Z","timestamp":1666314394000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-022-18839-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,20]]},"references-count":75,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["18839"],"URL":"https:\/\/doi.org\/10.1038\/s41598-022-18839-9","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,20]]},"assertion":[{"value":"1 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 August 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 October 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"17592"}}