research-article

LGFat-RGCN: Faster Attention with Heterogeneous RGCN for Medical ICD Coding Generation

Authors:

Xiaoxuan LiangAuthors Info & Claims

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

Pages 5428 - 5435

https://doi.org/10.1145/3581783.3612564

Published: 27 October 2023 Publication History

Abstract

With the increasing volume of healthcare data, automated International Classification of Diseases (ICD) has become increasingly relevant and is frequently regarded as a medical multi-label prediction problem. Current methods struggle to accurately classify medical diagnosis texts that represent deep and sparse categories. Unlike these works that model the label with code hierarchy or description for label prediction, we argue that the label generation with structural information can provide more comprehensive knowledge based on the observation that label synonyms and parent-child relationships in vary from their context in clinical contexts. In this study, we introduce \tool, a heterogeneous graph model with improved attention for automated ICD coding. Notably, our approach represents the model to consider this task as a labelled graph generation problem. Our enhanced attention mechanism boosts the model's capacity to learn from multi-relational heterogeneous graph representations. Additionally, we propose a discriminator for labelled graphs (LG) that computes the reward for each ICD code in the labelled graph generator. Our experimental findings demonstrate that our proposed model significantly outperforms all existing strong baseline methods and attains the best performance on three benchmark datasets.

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Cited By

Chen KDeng YChen QLi D(2024)AdHierNet: Enhancing Adversarial Robustness and Interpretability in Text Classification2024 6th International Conference on Natural Language Processing (ICNLP)10.1109/ICNLP60986.2024.10692972(41-45)Online publication date: 22-Mar-2024
https://doi.org/10.1109/ICNLP60986.2024.10692972
Yanlin Liu Jiayi Wang (2023)AI-Driven Health Advice: Evaluating the Potential of Large Language Models as Health AssistantsJournal of Computational Methods in Engineering Applications10.62836/jcmea.v3i1.030106(1-7)Online publication date: 6-Nov-2023
https://doi.org/10.62836/jcmea.v3i1.030106
Tang YLi C(2023)Exploring the Factors of Supply Chain Concentration in Chinese A-Share Listed EnterprisesJournal of Computational Methods in Engineering Applications10.62836/jcmea.v3i1.030105(1-17)Online publication date: 6-Nov-2023
https://doi.org/10.62836/jcmea.v3i1.030105

Index Terms

LGFat-RGCN: Faster Attention with Heterogeneous RGCN for Medical ICD Coding Generation
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

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cover image ACM Conferences

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

October 2023

9913 pages

ISBN:9798400701085

DOI:10.1145/3581783

General Chairs:
Abdulmotaleb El Saddik
University of Ottawa, Canada & MBZUAI, UAE
,
Tao Mei
HiDream.ai, China
,
Rita Cucchiara
University of Modena and Reggio Emilia, Italy
,
Program Chairs:
Marco Bertini
University of Florence, Italy
,
Diana Patricia Tobon Vallejo
Unversidad de Medellin, Colombia
,
Pradeep K. Atrey
University at Albany, State University of New York, USA
,
M. Shamim Hossain
M. Shamim Hossain (King Saud University, KSA

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Published: 27 October 2023

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National Natural Science Foundation of China
Open Research Fund from Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

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MM '23

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SIGMM

MM '23: The 31st ACM International Conference on Multimedia

October 29 - November 3, 2023

Ottawa ON, Canada

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Cited By

Chen KDeng YChen QLi D(2024)AdHierNet: Enhancing Adversarial Robustness and Interpretability in Text Classification2024 6th International Conference on Natural Language Processing (ICNLP)10.1109/ICNLP60986.2024.10692972(41-45)Online publication date: 22-Mar-2024
https://doi.org/10.1109/ICNLP60986.2024.10692972
Yanlin Liu Jiayi Wang (2023)AI-Driven Health Advice: Evaluating the Potential of Large Language Models as Health AssistantsJournal of Computational Methods in Engineering Applications10.62836/jcmea.v3i1.030106(1-7)Online publication date: 6-Nov-2023
https://doi.org/10.62836/jcmea.v3i1.030106
Tang YLi C(2023)Exploring the Factors of Supply Chain Concentration in Chinese A-Share Listed EnterprisesJournal of Computational Methods in Engineering Applications10.62836/jcmea.v3i1.030105(1-17)Online publication date: 6-Nov-2023
https://doi.org/10.62836/jcmea.v3i1.030105

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