Medical Code Assignment with Gated Convolution and Note-Code Interaction

Shaoxiong Ji, Shirui Pan, Pekka Marttinen

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

Abstract

Medical code assignment from clinical text is a fundamental task in clinical information system management. As medical notes are typically lengthy and the medical coding system’s code space is large, this task is a longstanding
challenge. Recent work applies deep neural network models to encode the medical notes and assign medical codes to clinical documents.
However, these methods are still ineffective as they do not fully encode and capture the lengthy and rich semantic information of medical notes nor explicitly exploit the interactions between the notes and codes. We propose a novel method, gated convolutional neural networks, and a note-code interaction (GatedCNN-NCI), for automatic medical code assignment to overcome these challenges. Our methods capture the rich semantic information of the lengthy clinical text for better representation by utilizing embedding injection and gated information propagation in the medical note encoding module. With a novel note-code interaction design and a graph message passing mechanism, we explicitly capture the underlying dependency between notes and codes, enabling effective code prediction. A weight sharing scheme is further designed to decrease the number of trainable parameters. Empirical experiments on real-world clinical datasets show that our proposed model outperforms state-of-the-art models in most cases, and our model size is on par with light-weighted baselines.
Original languageEnglish
Title of host publicationFindings of the Association for Computational Linguistics: ACL-IJCNLP 2021
PublisherAssociation for Computational Linguistics (ACL)
Pages1034-1043
Number of pages10
ISBN (Print)978-1-954085-54-1
DOIs
Publication statusPublished - 1 Aug 2021
MoE publication typeA4 Article in a conference publication
EventJoint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing - Bangkok, Thailand
Duration: 1 Aug 20216 Aug 2021

Publication series

NameAnnual Meeting of the Association for Computational Linguistics
PublisherAssociation for Computational Linguistics
ISSN (Print)0736-587X

Conference

ConferenceJoint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing
Abbreviated titleACL-IJCNLP
Country/TerritoryThailand
CityBangkok
Period01/08/202106/08/2021

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