Identification of the Risk Factors Associated with ICU Mortality

Abstract

The objective of this study is to identify the risk factors that influence the surgical and medical Intensive Care Unit (ICU) mortality. We considered data that was collected at Bay State Medical Center in Springfield, Massachusetts [1]. We developed statistical models that identify the risk factors associate with ICU mortality. In order to identify the risk factors without subjective bias, we explored multiple variable selection methods. We explored several methods including what we call manually picked best model, forward selection, backward elimination, and the least absolute shrinkage and selection operator (LASSO). We applied 5-fold cross validation on the final model of manually picked best model, forward selection and backward elimination and applied both validation set approach and 5-fold cross validation on LASSO to create confusion matrices and calculate the error rate of each method. Finally, we recommended the model for predicting ICU mortality with lowest misclassification error rate.