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A Knowledge Graph Embedding-based Approach to Predict the Adverse Drug Reactions using a Convolutional Neural Network

Author

Listed:
  • Juhua Wu

    (Guangdong University of Technology, Guangzhou 510520, Guangdong, P. R. China)

  • Ya Nie

    (Guangdong University of Technology, Guangzhou 510520, Guangdong, P. R. China)

  • Zheng Feng Liu

    (Guangdong University of Technology, Guangzhou 510520, Guangdong, P. R. China)

  • Lei Tao

    (Guangdong University of Technology, Guangzhou 510520, Guangdong, P. R. China)

  • Wen Zheng

    (Guangdong University of Technology, Guangzhou 510520, Guangdong, P. R. China)

Abstract

Our research is centred on drug knowledge discovery, involving the integration of drug knowledge graphs with machine learning techniques. To address the challenges of burdensome workload and inadequate classification accuracy associated with constructing individual models for each Adverse Drug Reaction (ADR), we developed and refined a Convolutional Neural Network (CNN) based on knowledge graph embedding (KGE) and deep learning methodologies. The outcomes of our study demonstrate that our proposed predictive model achieves remarkable precision and reliability, enabling comprehensive exploration of drug-reaction relationships. Our contributions introduce innovative models and techniques that drive the advancement of intelligent healthcare and demonstrate the scalability of data science applications in the medical domain.

Suggested Citation

  • Juhua Wu & Ya Nie & Zheng Feng Liu & Lei Tao & Wen Zheng, 2025. "A Knowledge Graph Embedding-based Approach to Predict the Adverse Drug Reactions using a Convolutional Neural Network," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 24(01), pages 1-20, February.
    • Handle: RePEc:wsi:jikmxx:v:24:y:2025:i:01:n:s0219649224501004
    DOI: 10.1142/S0219649224501004
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