{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T18:11:56Z","timestamp":1740161516398,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"abstract":"<jats:p>Clinical risk prediction of acute coronary syndrome (ACS) plays a critical role for clinical decision support, treatment management and quality of care assessment in ACS patients. Admission records contain a wealth of patient information in the early stages of hospitalization, which offers the opportunity to support the ACS risk prediction in a proactive manner. However, ACS patient risks aren't recorded in hospital admission records, thus impeding the construction of supervised risk prediction models. In our study, we propose a novel approach for ACS risk prediction, which employs a well-known ACS risk prediction model (GRACE) as the benchmark methods to stratify patient risks, and then utilizes a state-of-the-art supervised machine learning algorithm to establish our risk prediction models. The experiment was conducted with a collection of 3,643 ACS patient samples from a Chinese hospital. Our best model achieved 0.616 accuracy for risk prediction, which indicates our learned model can achieve a better performance than the benchmark GRACE model and can obtain significant improvement by mixing up patient samples that were manually labeled risks.<\/jats:p>","DOI":"10.3233\/978-1-61499-830-3-398","type":"book-chapter","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T16:48:36Z","timestamp":1740156516000},"source":"Crossref","is-referenced-by-count":0,"title":["Acute Coronary Syndrome Risk Prediction Based on GRACE Risk Score"],"prefix":"10.3233","author":[{"family":"Hu Danqing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Huang Zhengxing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Chan Tak-Ming","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Dong Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Lu Xudong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Duan Huilong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2017: Precision Healthcare through Informatics"],"original-title":[],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T17:29:10Z","timestamp":1740158950000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-829-7&spage=398&doi=10.3233\/978-1-61499-830-3-398"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-830-3-398","relation":{},"ISSN":["0926-9630"],"issn-type":[{"value":"0926-9630","type":"print"}],"subject":[],"published":{"date-parts":[[2017]]}}}