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.
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