Adversarial MACE Prediction After Acute Coronary Syndrome Using Electronic Health Records

Acute coronary syndrome (ACS), as an emergent and severe syndrome due to decreased blood flow in the coronary arteries, is a leading cause of death and serious long-term disability globally. ACS is usually caused by one of three problems: ST elevation myocardial infarction, non-ST elevation myocardial infarction, or unstable angina. Major adverse cardiac event (MACE) prediction, as a critical tool to estimate the likelihood an individual is at risk of ACS, has been widely adopted in the early prevention and intervention of ACS. Although valuable, existing MACE prediction models are designed to predict the overall probability of MACE occurrence for ACS patients, and lack the ability to look for insight into the disease to distinguish the different subtypes of ACS in a fine-grained manner. It is interesting to exploit the different subtypes of ACS and mine their private and shared underlying knowledge to improve the performance of MACE prediction. In this study, we propose utilizing a large volume of heterogeneous electronic health records for the application of MACE prediction. In detail, we address the multi-subtype-oriented MACE prediction for ACS as a multi-task learning (MTL) problem, present a MTL-based model to predict MACE of ACS patients with the different subtypes, and incorporate adversarial learning into the model to alleviate both the shared and private latent feature spaces of each subtype of ACS from interfering with each other. A real clinical dataset containing 2,863 ACS patient samples is collected from a Chinese hospital to validate the proposed model. Experimental results demonstrate that the prediction performance of our proposed model obtains a significant improvement, compared to single-subtype-oriented MACE prediction models.