  Healthcare data continues to flourish yet a relatively small portion, mostly
structured, is being utilized effectively for predicting clinical outcomes. The
rich subjective information available in unstructured clinical notes can
possibly facilitate higher discrimination but tends to be under-utilized in
mortality prediction. This work attempts to assess the gain in performance when
multiple notes that have been minimally preprocessed are used as an input for
prediction. A hierarchical architecture consisting of both convolutional and
recurrent layers is used to concurrently model the different notes compiled in
an individual hospital stay. This approach is evaluated on predicting
in-hospital mortality on the MIMIC-III dataset. On comparison to approaches
utilizing structured data, it achieved higher metrics despite requiring less
cleaning and preprocessing. This demonstrates the potential of unstructured
data in enhancing mortality prediction and signifies the need to incorporate
more raw unstructured data into current clinical prediction methods.
