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

Juhua Wu (), Ya Nie (), Zheng Feng Liu (), Lei Tao and Wen Zheng ()
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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

Journal of Information & Knowledge Management (JIKM), 2025, vol. 24, issue 01, 1-20

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.

Keywords: Knowledge graph; medication safety; convolutional neural network; adverse drug reaction (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0219649224501004

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