  Clinical notes contain a large amount of clinically valuable information that
is ignored in many clinical decision support systems due to the difficulty that
comes with mining that information. Recent work has found success leveraging
deep learning models for the prediction of clinical outcomes using clinical
notes. However, these models fail to provide clinically relevant and
interpretable information that clinicians can utilize for informed clinical
care. In this work, we augment a popular convolutional model with an attention
mechanism and apply it to unstructured clinical notes for the prediction of ICU
readmission and mortality. We find that the addition of the attention mechanism
leads to competitive performance while allowing for the straightforward
interpretation of predictions. We develop clear visualizations to present
important spans of text for both individual predictions and high-risk cohorts.
We then conduct a qualitative analysis and demonstrate that our model is
consistently attending to clinically meaningful portions of the narrative for
all of the outcomes that we explore.
