  Mortality risk is a major concern to patients have just been discharged from
the intensive care unit (ICU). Many studies have been directed to construct
machine learning models to predict such risk. Although these models are highly
accurate, they are less amenable to interpretation and clinicians are typically
unable to gain further insights into the patients' health conditions and the
underlying factors that influence their mortality risk. In this paper, we use
patients' profiles extracted from the MIMIC-III clinical database to construct
risk calculators based on different machine learning techniques such as
logistic regression, decision trees, random forests and multilayer perceptrons.
We perform an extensive benchmarking study that compares the most salient
features as predicted by various methods. We observe a high degree of agreement
across the considered machine learning methods; in particular, the cardiac
surgery recovery unit, age, and blood urea nitrogen levels are commonly
predicted to be the most salient features for determining patients' mortality
risks. Our work has the potential for clinicians to interpret risk predictions.
