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Graph Neural Network Based Multi-Label Hierarchical Classification for Disease Predictions in General Practice.

Shengqiang Chi1, Yuqing Wang1, Ying Zhang1

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|January 25, 2024
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This study introduces a graph neural network model to improve general disease diagnosis by primary care physicians. The new method enhances diagnostic accuracy using electronic health records and medical knowledge.

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General practicedeep learningdiagnosis predictiongraph neural networkmulti-label classification

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Decision Support

Background:

  • General practitioners (GPs) play a crucial role in early disease detection and intervention.
  • Current primary care faces challenges with insufficient GP experience, leading to low diagnostic accuracy.
  • Accurate and timely diagnosis is essential for effective patient management and referral.

Purpose of the Study:

  • To develop an advanced diagnostic tool for general practitioners to improve disease detection rates.
  • To leverage medical knowledge and electronic health record (EHR) data for enhanced diagnostic capabilities.
  • To create an interpretable disease prediction model that aids clinical decision-making.

Main Methods:

  • A multi-label hierarchical classification method utilizing graph neural networks (GNNs) was proposed.
  • The model integrates structured medical knowledge with unstructured EHR data.
  • A large-scale dataset of 231,783 patient visits from EHR was used for model training and validation.

Main Results:

  • The proposed GNN-based model demonstrated superior performance compared to baseline models.
  • The model achieved a top-3 recall of 0.865 in the general disease prediction task.
  • Experimental results confirmed the model's effectiveness in improving diagnostic accuracy.

Conclusions:

  • The developed model significantly enhances the diagnostic capabilities of general practitioners.
  • Integration of medical knowledge and EHR data via GNNs offers a promising approach for disease prediction.
  • The model's interpretability facilitates clinician trust and understanding of diagnostic recommendations.