BACKGROUND: Main adverse cardiac events (MACE) are essentially composite 
endpoints for assessing safety and efficacy of treatment processes of acute 
coronary syndrome (ACS) patients. Timely prediction of MACE is highly valuable 
for improving the effects of ACS treatments. Most existing tools are specific to 
predict MACE by mainly using static patient features and neglecting dynamic 
treatment information during learning.
METHODS: We address this challenge by developing a deep learning-based approach 
to utilize a large volume of heterogeneous electronic health record (EHR) for 
predicting MACE after ACS. Specifically, we obtain the deep representation of 
dynamic treatment features from EHR data, using the bidirectional recurrent 
neural network. And then, the extracted latent representation of treatment 
features can be utilized to predict whether a patient occurs MACE in his or her 
hospitalization.
RESULTS: We validate the effectiveness of our approach on a clinical dataset 
containing 2930 ACS patient samples with 232 static feature types and 2194 
dynamic feature types. The performance of our best model for predicting MACE 
after ACS remains robust and reaches 0.713 and 0.764 in terms of AUC and 
Accuracy, respectively, and has over 11.9% (1.2%) and 1.9% (7.5%) performance 
gain of AUC (Accuracy) in comparison with both logistic regression and a boosted 
resampling model presented in our previous work, respectively. The results are 
statistically significant.
CONCLUSIONS: We hypothesize that our proposed model adapted to leverage dynamic 
treatment information in EHR data appears to boost the performance of MACE 
prediction for ACS, and can readily meet the demand clinical prediction of other 
diseases, from a large volume of EHR in an open-ended fashion.