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Medical code assignment, which predicts medical codes from clinical texts, isa fundamental task of intelligent medical information systems. The emergence ofdeep models in natural language processing has boosted the development ofautomatic assignment methods. However, recent advanced neural architectureswith flat convolutions or multi-channel feature concatenation ignore thesequential causal constraint within a text sequence and may not learnmeaningful clinical text representations, especially for lengthy clinical noteswith long-term sequential dependency. This paper proposes a DilatedConvolutional Attention Network (DCAN), integrating dilated convolutions,residual connections, and label attention, for medical code assignment. Itadopts dilated convolutions to capture complex medical patterns with areceptive field which increases exponentially with dilation size. Experimentson a real-world clinical dataset empirically show that our model improves thestate of the art.

Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text