  Method: We develop CNN-based methods for automatic ICD coding based on
clinical text from intensive care unit (ICU) stays. We come up with the Shallow
and Wide Attention convolutional Mechanism (SWAM), which allows our model to
learn local and low-level features for each label. The key idea behind our
model design is to look for the presence of informative snippets in the
clinical text that correlated with each code, and we infer that there exists a
correspondence between "informative snippet" and convolution filter. Results:
We evaluate our approach on MIMIC-III, an open-access dataset of ICU medical
records. Our approach substantially outperforms previous results on top-50
medical code prediction on MIMIC-III dataset. We attribute this improvement to
SWAM, by which the wide architecture gives the model ability to more
extensively learn the unique features of different codes, and we prove it by
ablation experiment. Besides, we perform manual analysis of the performance
imbalance between different codes, and preliminary conclude the characteristics
that determine the difficulty of learning specific codes. Conclusions: We
present SWAM, an explainable CNN approach for multi-label document
classification, which employs a wide convolution layer to learn local and
low-level features for each label, yields strong improvements over previous
metrics on the ICD-9 code prediction task, while providing satisfactory
explanations for its internal mechanics.
