Abstract:
Risk stratification at the time of hospital admission is of paramount significance in triaging
the patients and providing timely care. In the present study, we aim at predicting multiple clinical
outcomes using the data recorded during admission to a cardiac care unit via an optimized machine
learning method. This study involves a total of 11,498 patients admitted to a cardiac care unit over
two years. Patient demographics, admission type (emergency or outpatient), patient history, lab tests,
and comorbidities were used to predict various outcomes. We employed a fully connected neural
network architecture and optimized the models for various subsets of input features. Using 10-fold
cross-validation, our optimized machine learning model predicted mortality with a mean area under
the receiver operating characteristic curve (AUC) of 0.967 (95% confidence interval (CI): 0.963–0.972),
heart failure AUC of 0.838 (CI: 0.825–0.851), ST-segment elevation myocardial infarction AUC of 0.832
(CI: 0.821–0.842), pulmonary embolism AUC of 0.802 (CI: 0.764–0.84), and estimated the duration
of stay (DOS) with a mean absolute error of 2.543 days (CI: 2.499–2.586) of data with a mean and
median DOS of 6.35 and 5.0 days, respectively. Further, we objectively quantified the importance
of each feature and its correlation with the clinical assessment of the corresponding outcome. The
proposed method accurately predicts various cardiac outcomes and can be used as a clinical decision
support system to provide timely care and optimize hospital resources.
