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EHR coding with hybrid attention and features propagation on disease knowledge graph.

Tianhan Xu1, Bin Li1, Ling Chen1

  • 1School of Information Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China; Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou, 225127, Jiangsu, China.

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|June 23, 2024
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Summary

This study introduces KGENet, a novel model that enhances electronic health record (EHR) coding by utilizing disease knowledge graphs. KGENet improves accuracy and explainability in assigning International Classification of Diseases (ICD) codes.

Keywords:
Disease knowledge graphEHR codingExplainabilityGraph propagationHybrid attentionICD

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare

Background:

  • Electronic Health Record (EHR) coding is crucial for medical applications but faces challenges due to long text, large label space, and unbalanced distributions.
  • Existing methods overlook disease attributes and relationships inherent in International Classification of Diseases (ICD) codes.

Purpose of the Study:

  • To propose an end-to-end model, KGENet, for enhanced EHR coding by integrating knowledge graphs.
  • To address the limitations of previous studies by incorporating multi-view disease attributes and relationships.

Main Methods:

  • Constructed a disease knowledge graph detailing multi-view attributes and relationships of ICD codes.
  • Employed a long sequence encoder for EHR document representation.
  • Utilized hybrid attention and graph propagation for knowledge enhancement within KGENet.

Main Results:

  • KGENet demonstrated superior performance on the MIMIC-III benchmark dataset compared to state-of-the-art models.
  • The model achieved improvements in both predictive effectiveness and explainability.
  • Attribute-aware and relationship-augmented explanations were generated based on the disease knowledge graph.

Conclusions:

  • KGENet effectively leverages disease knowledge graphs to enhance EHR coding.
  • The proposed model offers improved accuracy and provides valuable explainability for predictions.
  • This approach represents a significant advancement in automatic EHR coding and medical informatics.