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

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.