  Interpretability of machine learning models is critical for data-driven
precision medicine efforts. However, highly predictive models are generally
complex and are difficult to interpret. Here using Model-Agnostic Explanations
algorithm, we show that complex models such as random forest can be made
interpretable. Using MIMIC-II dataset, we successfully predicted ICU mortality
with 80% balanced accuracy and were also were able to interpret the relative
effect of the features on prediction at individual level.
