Research on Adverse Drug Reaction Prediction Model Combining Knowledge Graph Embedding and Deep Learning
<|reference_start|>Research on Adverse Drug Reaction Prediction Model Combining Knowledge Graph Embedding and Deep Learning: In clinical treatment, identifying potential adverse reactions of drugs can help assist doctors in making medication decisions. In response to the problems in previous studies that features are high-dimensional and sparse, independent prediction models need to be constructed for each adverse reaction of drugs, and the prediction accuracy is low, this paper develops an adverse drug reaction prediction model based on knowledge graph embedding and deep learning, which can predict experimental results. Unified prediction of adverse drug reactions covered. Knowledge graph embedding technology can fuse the associated information between drugs and alleviate the shortcomings of high-dimensional sparsity in feature matrices, and the efficient training capabilities of deep learning can improve the prediction accuracy of the model. This article builds an adverse drug reaction knowledge graph based on drug feature data; by analyzing the embedding effect of the knowledge graph under different embedding strategies, the best embedding strategy is selected to obtain sample vectors; and then a convolutional neural network model is constructed to predict adverse reactions. The results show that under the DistMult embedding model and 400-dimensional embedding strategy, the convolutional neural network model has the best prediction effect; the average accuracy, F_1 score, recall rate and area under the curve of repeated experiments are better than the methods reported in the literature. The obtained prediction model has good prediction accuracy and stability, and can provide an effective reference for later safe medication guidance.<|reference_end|>
@article{li2024research,
    title={Research on Adverse Drug Reaction Prediction Model Combining Knowledge
  Graph Embedding and Deep Learning},
    author={Yufeng Li, Wenchao Zhao, Bo Dang, Xu Yan, Weimin Wang, Min Gao,
  Mingxuan Xiao},
    journal={arXiv preprint arXiv:2407.16715},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.16715},
    primaryClass={q-bio.QM cs.AI cs.LG}
}