Acute Coronary Syndrome Risk Prediction Based on GRACE Risk Score

摘要：

　　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 arent 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.

关键词： Risk Assessment Acute Coronary Syndrome Supervised Machine Learning

作者: Danqing Hu Zhengxing Huang Tak-Ming Chan Wei Dong Xudong Lu Huilong Duan

作者单位: College of Biomedical Engineering and Instrument Science,Zhejiang University,Hangzhou,Zhejiang,China Health Systems,Philips Research China,Shanghai,China Department of Cardiology,Chinese PLA General Hospital,Beijing,China

会议类型: 国际会议

会议名称: 第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)

会议地点: 苏州

会议语种:英文

页码: 398-402

在线出版日期: 2017-08-21（万方平台首次上网日期，不代表论文的发表时间）