{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,7]],"date-time":"2026-06-07T01:27:05Z","timestamp":1780795625475,"version":"3.54.1"},"reference-count":26,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,7,5]],"date-time":"2021-07-05T00:00:00Z","timestamp":1625443200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational and Mathematical Methods in Medicine"],"published-print":{"date-parts":[[2021,7,5]]},"abstract":"<jats:p>Accurate risk assessment of high-risk patients is essential in clinical practice. However, there is no practical method to predict or monitor the prognosis of patients with ST-segment elevation myocardial infarction (STEMI) complicated by hyperuricemia. We aimed to evaluate the performance of different machine learning models for the prediction of 1-year mortality in STEMI patients with hyperuricemia. We compared five machine learning models (logistic regression, <jats:inline-formula>\n                     <a:math xmlns:a=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\">\n                        <a:mi>k<\/a:mi>\n                     <\/a:math>\n                  <\/jats:inline-formula>-nearest neighbor, CatBoost, random forest, and XGBoost) with the traditional global (GRACE) risk score for acute coronary event registrations. We registered patients aged &gt;18 years diagnosed with STEMI and hyperuricemia at the Affiliated Hospital of Zunyi Medical University between January 2016 and January 2020. Overall, 656 patients were enrolled (average age, <jats:inline-formula>\n                     <c:math xmlns:c=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\">\n                        <c:mn>62.5<\/c:mn>\n                        <c:mo>\u00b1<\/c:mo>\n                        <c:mn>13.6<\/c:mn>\n                        <c:mtext>\u2009<\/c:mtext>\n                        <c:mtext>years<\/c:mtext>\n                     <\/c:math>\n                  <\/jats:inline-formula>; 83.6%, male). All patients underwent emergency percutaneous coronary intervention. We evaluated the performance of five machine learning classifiers and the GRACE risk model in predicting 1-year mortality. The area under the curve (AUC) of the six models, including the GRACE risk model, ranged from 0.75 to 0.88. Among all the models, CatBoost had the highest predictive accuracy (0.89), AUC (0.87), precision (0.84), and F1 value (0.44). After hybrid sampling technique optimization, CatBoost had the highest accuracy (0.96), AUC (0.99), precision (0.95), and F1 value (0.97). Machine learning algorithms, especially the CatBoost model, can accurately predict the mortality associated with STEMI complicated by hyperuricemia after a 1-year follow-up.<\/jats:p>","DOI":"10.1155\/2021\/7252280","type":"journal-article","created":{"date-parts":[[2021,7,5]],"date-time":"2021-07-05T21:05:09Z","timestamp":1625519109000},"page":"1-9","source":"Crossref","is-referenced-by-count":33,"title":["Clinical Feature-Based Machine Learning Model for 1-Year Mortality Risk Prediction of ST-Segment Elevation Myocardial Infarction in Patients with Hyperuricemia: A Retrospective Study"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4352-0658","authenticated-orcid":true,"given":"Zhixun","family":"Bai","sequence":"first","affiliation":[{"name":"Program of Artificial Intelligence in Medicine, College of Medicine, Soochow University, Suzhou 215123, China"},{"name":"Department of Internal Medicine, The Second Affiliated Hospital of Zunyi Medical University, Zunyi 563000, China"},{"name":"Department of Cardiology, Affiliated Hospital of Zunyi Medical University, Zunyi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Pathology, Zunyi Medical and Pharmaceutical College, Zunyi 563006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Affiliated Hospital of Zunyi Medical University, Zunyi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Affiliated Hospital of Zunyi Medical University, Zunyi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijiang","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Affiliated Hospital of Zunyi Medical University, Zunyi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ranzun","family":"Zhao","sequence":"additional","affiliation":[{"name":"Program of Artificial Intelligence in Medicine, College of Medicine, Soochow University, Suzhou 215123, China"},{"name":"Department of Cardiology, Affiliated Hospital of Zunyi Medical University, Zunyi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenglong","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Affiliated Hospital of Zunyi Medical University, Zunyi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5316-2351","authenticated-orcid":true,"given":"Bei","family":"Shi","sequence":"additional","affiliation":[{"name":"Program of Artificial Intelligence in Medicine, College of Medicine, Soochow University, Suzhou 215123, China"},{"name":"Department of Cardiology, Affiliated Hospital of Zunyi Medical University, Zunyi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1702090"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1053\/j.ajkd.2017.08.017"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1093\/ajh\/hpaa044"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1016\/j.cca.2018.05.046"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1007\/s11883-017-0661-2"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijcard.2017.05.113"},{"issue":"3","key":"7","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1007\/s12529-016-9583-6","article-title":"2016 European guidelines on cardiovascular disease prevention in clinical practice : the Sixth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts)","volume":"24","author":"F PM","year":"2017","journal-title":"International Journal of Behavioral Medicine"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.21037\/atm.2019.10.110"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijcard.2017.04.027"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1016\/j.atherosclerosis.2018.02.003"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1016\/j.visinf.2019.03.002"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.114.014508"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1001\/archinte.163.19.2345"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.2147\/TCRM.S236498"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehw302"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btr597"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.5543\/tkda.2019.84401"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.5543\/tkda.2017.11126"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1177\/0003319714568516"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1080\/03007995.2017.1372157"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.2215\/CJN.11041013"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.1038\/ki.2013.353"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1186\/s13063-020-04505-w"},{"key":"24","doi-asserted-by":"publisher","DOI":"10.6515\/ACS.201801_34(1).20170730B"},{"key":"25","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.g7594"},{"key":"26","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-020-00468-x"}],"container-title":["Computational and Mathematical Methods in Medicine"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2021\/7252280.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2021\/7252280.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2021\/7252280.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,5]],"date-time":"2021-07-05T21:05:23Z","timestamp":1625519123000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/cmmm\/2021\/7252280\/"}},"subtitle":[],"editor":[{"given":"Giovanni","family":"D Addio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2021,7,5]]},"references-count":26,"alternative-id":["7252280","7252280"],"URL":"https:\/\/doi.org\/10.1155\/2021\/7252280","relation":{},"ISSN":["1748-6718","1748-670X"],"issn-type":[{"value":"1748-6718","type":"electronic"},{"value":"1748-670X","type":"print"}],"subject":[],"published":{"date-parts":[[2021,7,5]]}}}