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

In current clinical practice, score-based mortality prediction systems, such as the series of the acute Predicting the risk of mortality for patients with acute physiology and chronic health evaluation (APACHE) scoring myocardial infarction (AMI) using electronic health records system, are widely used to help determine the treatment or (EHRs) data can help identify risky patients who might need medicine should be given to patients admitted into intensive more tailored care. In our previous work, we built care units (ICUs) [10]. Nevertheless, these scoring systems computational models to predict one-year mortality of patients have significant limitations, e.g., 1) they are often restricted to admitted to an intensive care unit (ICU) with AMI or post only few predictors; 2) they have poor generalizability and may myocardial infarction syndrome. Our prior work only used the be less precise when applied to specific subpopulations other structured clinical data from MIMIC-III, a publicly available than the original population used for the initial development; ICU clinical database. In this study, we enhanced our work by and 3) they need to be periodically recalibrated to reflect adding the word embedding features from free-text discharge changes in clinical practice and patient demographics [6].