  Medical code assignment, which predicts medical codes from clinical texts, is
a fundamental task of intelligent medical information systems. The emergence of
deep models in natural language processing has boosted the development of
automatic assignment methods. However, recent advanced neural architectures
with flat convolutions or multi-channel feature concatenation ignore the
sequential causal constraint within a text sequence and may not learn
meaningful clinical text representations, especially for lengthy clinical notes
with long-term sequential dependency. This paper proposes a Dilated
Convolutional Attention Network (DCAN), integrating dilated convolutions,
residual connections, and label attention, for medical code assignment. It
adopts dilated convolutions to capture complex medical patterns with a
receptive field which increases exponentially with dilation size. Experiments
on a real-world clinical dataset empirically show that our model improves the
state of the art.
