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

Enhancing Prediction Models for One-Year Mortality in Patients with Acute Myocardial Infarction and Post Myocardial Infarction Syndrome