Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text

TL;DR

This paper introduces a Dilated Convolutional Attention Network (DCAN) that effectively captures long-term dependencies in clinical texts for medical code assignment, outperforming previous models.

Contribution

It proposes a novel neural architecture combining dilated convolutions, residual connections, and label attention for improved clinical text representation.

Findings

DCAN outperforms existing models on real-world clinical datasets.

The use of dilated convolutions captures complex medical patterns.

Model achieves state-of-the-art accuracy in medical code assignment.

Abstract

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.