article · 2020

Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text

Shaoxiong Ji, Erik Cambria, Pekka Marttinen

Cited by 18Open access

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.

Cite this paper

APA 7

Ji, S., Cambria, E., & Marttinen, P. (2020). Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text. https://doi.org/10.18653/v1/2020.clinicalnlp-1.8

MLA 9

Ji, Shaoxiong, et al. “Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text.” 2020, pp. 73–78, https://doi.org/10.18653/v1/2020.clinicalnlp-1.8.

Chicago

Ji, Shaoxiong, Erik Cambria, and Pekka Marttinen. 2020. “Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text.” https://doi.org/10.18653/v1/2020.clinicalnlp-1.8.

Harvard

Ji, S., Cambria, E. and Marttinen, P. (2020) ‘Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text’. Available at: https://doi.org/10.18653/v1/2020.clinicalnlp-1.8.

BibTeX

@article{ji2020dilated,
  title = {{Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text}},
  author = {Ji, Shaoxiong and Cambria, Erik and Marttinen, Pekka},
  pages = {73--78},
  year = {2020},
  doi = {10.18653/v1/2020.clinicalnlp-1.8}
}

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