  Predicting the risk of mortality for patients with acute myocardial
infarction (AMI) using electronic health records (EHRs) data can help identify
risky patients who might need more tailored care. In our previous work, we
built computational models to predict one-year mortality of patients admitted
to an intensive care unit (ICU) with AMI or post myocardial infarction
syndrome. Our prior work only used the structured clinical data from MIMIC-III,
a publicly available ICU clinical database. In this study, we enhanced our work
by adding the word embedding features from free-text discharge summaries. Using
a richer set of features resulted in significant improvement in the performance
of our deep learning models. The average accuracy of our deep learning models
was 92.89% and the average F-measure was 0.928. We further reported the impact
of different combinations of features extracted from structured and/or
unstructured data on the performance of the deep learning models.
