  A ubiquitous task in processing electronic medical data is the assignment of
standardized codes representing diagnoses and/or procedures to free-text
documents such as medical reports. This is a difficult natural language
processing task that requires parsing long, heterogeneous documents and
selecting a set of appropriate codes from tens of thousands of
possibilities---many of which have very few positive training samples. We
present a deep learning system that advances the state of the art for the
MIMIC-III dataset, achieving a new best micro F1-measure of 55.85\%,
significantly outperforming the previous best result (Mullenbach et al. 2018).
We achieve this through a number of enhancements, including two major novel
contributions: multi-view convolutional channels, which effectively learn to
adjust kernel sizes throughout the input; and attention regularization,
mediated by natural-language code descriptions, which helps overcome sparsity
for thousands of uncommon codes. These and other modifications are selected to
address difficulties inherent to both automated coding specifically and deep
learning generally. Finally, we investigate our accuracy results in detail to
individually measure the impact of these contributions and point the way
towards future algorithmic improvements.
