  To improve the performance of Intensive Care Units (ICUs), the field of
bio-statistics has developed scores which try to predict the likelihood of
negative outcomes. These help evaluate the effectiveness of treatments and
clinical practice, and also help to identify patients with unexpected outcomes.
However, they have been shown by several studies to offer sub-optimal
performance. Alternatively, Deep Learning offers state of the art capabilities
in certain prediction tasks and research suggests deep neural networks are able
to outperform traditional techniques. Nevertheless, a main impediment for the
adoption of Deep Learning in healthcare is its reduced interpretability, for in
this field it is crucial to gain insight on the why of predictions, to assure
that models are actually learning relevant features instead of spurious
correlations. To address this, we propose a deep multi-scale convolutional
architecture trained on the Medical Information Mart for Intensive Care III
(MIMIC-III) for mortality prediction, and the use of concepts from coalitional
game theory to construct visual explanations aimed to show how important these
inputs are deemed by the network. Our results show our model attains state of
the art performance while remaining interpretable. Supporting code can be found
at https://github.com/williamcaicedo/ISeeU.
