  Acute myocardial infarction (AMI) is one of the most severe manifestation of
coronary artery disease. ST-segment elevation myocardial infarction (STEMI) is
the most serious type of AMI. We proposed to develop a machine learning
algorithm based on the home page of electronic medical record (HPEMR) for
predicting in-hospital mortality of patients with STEMI in the early stage.
Methods: This observational study applied clinical information collected
between 2013 and 2017 from 7 tertiary hospitals in Shenzhen, China. The
patients' STEMI data were used to train 4 different machine learning algorithms
to predict in-hospital mortality among the patients with STEMI, including
Logistic Regression, Support Vector Machine, Gradient Boosting Decision Tree,
and Artificial Neuron network. Results: A total of 5865 patients with STEMI
were enrolled in our study. The model was developed by considering 3 types of
variables, which included demographic data, diagnosis and comorbidities, and
hospitalization information basing on HPEMR. The association of selected
features using univariant logistic regression was reported. Specially, for the
comorbidities, atrial fibrillation (OR: 11.0; 95% CI: 5.64 - 20.2), acute renal
failure (OR: 9.75; 95% CI: 3.81 - 25.0), type 2 diabetic nephropathy (OR: 5.45;
95% CI: 1.57 - 19.0), acute heart failure (OR: 6.05; 95% CI: 1.99 - 14.9), and
cardiac function grade IV (OR: 28.6; 95% CI: 20.6 - 39.6) were found to be
associated with a high odds of death. Within the test dataset, our model showed
a good discrimination ability as measured by area under the receiver operating
characteristic curve (AUC; 0.879) (95% CI: 0.825 - 0.933).
