  Good predictors of ICU Mortality have the potential to identify high-risk
patients earlier, improve ICU resource allocation, or create more accurate
population-level risk models. Machine learning practitioners typically make
choices about how to represent features in a particular model, but these
choices are seldom evaluated quantitatively. This study compares the
performance of different representations of clinical event data from MIMIC II
in a logistic regression model to predict 36-hour ICU mortality. The most
common representations are linear (normalized counts) and binary (yes/no).
These, along with a new representation termed "hill", are compared using both
L1 and L2 regularization. Results indicate that the introduced "hill"
representation outperforms both the binary and linear representations, the hill
representation thus has the potential to improve existing models of ICU
mortality.
