research-article

Multi-aspect Understanding with Cooperative Graph Attention Networks for Medical Dialogue Information Extraction

Authors:

Haifeng WuAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology, Volume 14, Issue 6

Article No.: 103, Pages 1 - 18

https://doi.org/10.1145/3620675

Published: 14 November 2023 Publication History

Abstract

Medical dialogue information extraction is an important but challenging task for Electronic Medical Records. Existing medical information extraction methods ignore the crucial information of sentence and multi-level dependency in dialogue, which limits their effectiveness for capturing essential medical information. To address these issues, we present a novel Multi-aspect Understanding with Cooperative Graph Attention Networks for Medical Dialogue Information Extraction to capture multi-aspect sentence information and multi-level dependency information from the dialogue. First, we propose the multi-aspect sentence encoder to capture various features from different perspectives. Second, we propose double graph attention networks to model the dependency features from intra-window and inter-window, respectively. Extensive experiments on a benchmark dataset have well-validated the effectiveness of the proposed method.

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Cited By

Jian MBai YFu XGuo JShi GWu L(2024)Counterfactual Graph Convolutional Learning for Personalized RecommendationACM Transactions on Intelligent Systems and Technology10.1145/365563215:4(1-20)Online publication date: 18-Jun-2024
https://dl.acm.org/doi/10.1145/3655632
Li QHuang FGe LZhao J(2024)DIPE: a diagnosis-assisted inquiry point extractor towards medical dialoguesApplied Intelligence10.1007/s10489-024-06138-x55:3Online publication date: 27-Dec-2024
https://dl.acm.org/doi/10.1007/s10489-024-06138-x
Cheng ZZhu YWang SXin TZhang H(undefined)Cascaded Alternating Refinement Transformer for Few-shot Medical Image SegmentationACM Transactions on Intelligent Systems and Technology10.1145/3709145
https://dl.acm.org/doi/10.1145/3709145

Index Terms

Multi-aspect Understanding with Cooperative Graph Attention Networks for Medical Dialogue Information Extraction
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction

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cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 14, Issue 6

December 2023

493 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3632517

Editor:
Huan Liu
Arizona State University, USA

Issue’s Table of Contents

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Publication History

Published: 14 November 2023

Online AM: 05 September 2023

Accepted: 11 August 2023

Revised: 30 March 2023

Received: 17 August 2022

Published in TIST Volume 14, Issue 6

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National Natural Science Foundation of China
MOE (Ministry of Education in China) Project of Humanities and Social Sciences
Yunnan Natural Science Foundation of China

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Cited By

Jian MBai YFu XGuo JShi GWu L(2024)Counterfactual Graph Convolutional Learning for Personalized RecommendationACM Transactions on Intelligent Systems and Technology10.1145/365563215:4(1-20)Online publication date: 18-Jun-2024
https://dl.acm.org/doi/10.1145/3655632
Li QHuang FGe LZhao J(2024)DIPE: a diagnosis-assisted inquiry point extractor towards medical dialoguesApplied Intelligence10.1007/s10489-024-06138-x55:3Online publication date: 27-Dec-2024
https://dl.acm.org/doi/10.1007/s10489-024-06138-x
Cheng ZZhu YWang SXin TZhang H(undefined)Cascaded Alternating Refinement Transformer for Few-shot Medical Image SegmentationACM Transactions on Intelligent Systems and Technology10.1145/3709145
https://dl.acm.org/doi/10.1145/3709145

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