research-article

Knowledge-Enhanced Relation Extraction in Chinese EMRs

Authors:

Chenghao Zhang,

Kunli ZhangAuthors Info & Claims

MLNLP '22: Proceedings of the 2022 5th International Conference on Machine Learning and Natural Language Processing

Pages 196 - 201

https://doi.org/10.1145/3578741.3578781

Published: 06 March 2023 Publication History

Abstract

Electronic Medical Records (EMRs) is one of the important data sources of clinical information. Relation extraction is a key step to extract rich medical knowledge from EMRs, which has been studied by many scholars. However, there are some problems in EMRs corpus, such as entity nesting and relation overlapping, which make it difficult to achieve ideal results in EMRs relation extraction task. Previous studies rarely considered the fusion of knowledge graph containing rich and valuable structured knowledge, which leads to semantic ambiguity and other issues. Aiming at the above problems, Relation Extraction model based on Knowledge Graph and Chinese character Radical information(RE-KGR) model is proposed in this paper to study the relation extraction of EMRs in diabetic patients. Firstly, knowledge information is extracted from knowledge graph and embedded by GCN. At the same time, the corresponding radical features of Chinese characters are fused to enhance the semantic information of the input text. Compared with other baseline models, the DEMRC and DiaKG experiments of EMRs datasets of diabetic patients were improved by 1.32% and 2.19%.

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Cited By

Yang LChen HLi ZDing XWu X(2024)Give us the Facts: Enhancing Large Language Models With Knowledge Graphs for Fact-Aware Language ModelingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.336045436:7(3091-3110)Online publication date: Jul-2024
https://doi.org/10.1109/TKDE.2024.3360454

Index Terms

Knowledge-Enhanced Relation Extraction in Chinese EMRs
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction

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Knowledge-Enhanced Relation Extraction for Chinese EMRs
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MLNLP '22: Proceedings of the 2022 5th International Conference on Machine Learning and Natural Language Processing

December 2022

406 pages

ISBN:9781450399067

DOI:10.1145/3578741

Copyright © 2022 ACM.

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Published: 06 March 2023

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MLNLP 2022

MLNLP 2022: 2022 5th International Conference on Machine Learning and Natural Language Processing

December 23 - 25, 2022

Sanya, China

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Cited By

Yang LChen HLi ZDing XWu X(2024)Give us the Facts: Enhancing Large Language Models With Knowledge Graphs for Fact-Aware Language ModelingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.336045436:7(3091-3110)Online publication date: Jul-2024
https://doi.org/10.1109/TKDE.2024.3360454

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