Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text

Shaoxiong Ji, Erik Cambria, Pekka Marttinen

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


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.
Original languageEnglish
Title of host publicationProceedings of the 3rd Clinical Natural Language Processing Workshop
PublisherAssociation for Computational Linguistics (ACL)
ISBN (Electronic)978-1-952148-74-3
Publication statusPublished - 2020
MoE publication typeA4 Article in a conference publication
EventClinical Natural Language Processing Workshop - Virtual, Online
Duration: 19 Nov 202019 Nov 2020


WorkshopClinical Natural Language Processing Workshop
CityVirtual, Online


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