Prediction of Adverse Drug Reactions Based on Knowledge Graph Embedding and Deep Learning

Wu Ju-hua; Li Jun-feng; Tao Lei

doi:10.12052/gdutxb.230031

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Prediction of Adverse Drug Reactions Based on Knowledge Graph Embedding and Deep Learning

更新时间：2026-05-27

- Prediction of Adverse Drug Reactions Based on Knowledge Graph Embedding and Deep Learning
- Vol. 41, Issue 1, Pages: 19-26, 40(2024)
- 作者机构：
  
  广东工业大学管理学院,广东,广州,510520
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.230031
  CLC： TP399
- Online First：25 January 2024，
  
  Published：2024
- 稿件说明：
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吴菊华, 李俊锋, 陶雷. 基于知识图谱嵌入与深度学习的药物不良反应预测[J]. 广东工业大学学报, 2024, 41(1): 19-26, 40.

Wu Ju-hua, Li Jun-feng, Tao Lei. Prediction of Adverse Drug Reactions Based on Knowledge Graph Embedding and Deep Learning[J]. 2024, 41(1): 19-26, 40.
吴菊华, 李俊锋, 陶雷. 基于知识图谱嵌入与深度学习的药物不良反应预测[J]. 广东工业大学学报, 2024, 41(1): 19-26, 40. DOI： 10.12052/gdutxb.230031.

Wu Ju-hua, Li Jun-feng, Tao Lei. Prediction of Adverse Drug Reactions Based on Knowledge Graph Embedding and Deep Learning[J]. 2024, 41(1): 19-26, 40. DOI： 10.12052/gdutxb.230031.

摘要

识别药物潜在的不良反应，有助于辅助医生进行临床用药决策。针对以往研究的特征高维稀疏、需要为每种不良反应构建独立预测模型且预测精度较低的问题，本文开发一种基于知识图谱嵌入和深度学习的药物不良反应预测模型，能够对实验所覆盖的不良反应进行统一预测。一方面，知识图谱及其嵌入技术能够融合药物之间的关联信息，缓解特征矩阵高维稀疏的不足；另一方面，深度学习的高效训练能力能够提升模型的预测精度。本文使用药物特征数据构建药物不良反应知识图谱；通过分析不同嵌入策略下知识图谱的嵌入效果，选择最佳嵌入策略以获得样本向量；然后构建卷积神经网络模型对不良反应进行预测。结果表明，在DistMult嵌入模型和400维嵌入策略下，卷积神经网络模型预测效果最佳；重复实验的准确率、

分数、召回率和曲线下面积的平均值分别为0.887、0.890、0.913和0.957，优于文献报道中的方法。所得预测模型具有较好的预测精度和稳定性，可以为安全用药提供有效参考。

Abstract

Identifying potential adverse reactions of drugs can help doctors make clinical medication decisions. In view of the high-dimensional sparse features of previous studies and low prediction accuracy in constructing an independent prediction model for each adverse reaction

a prediction model of adverse reactions based on knowledge graph embedding and deep learning is developed

which can uniformly predict the adverse reactions covered by the experiment. On the one hand

knowledge graph and its embedding technology can fuse the correlation information between drugs a

nd alleviate the deficiency of high-dimensional sparse feature matrix. On the other hand

the efficient training ability of deep learning can improve the prediction accuracy. In the study

drug characteristic data is used to construct a knowledge graph of adverse drug reactions; by analyzing the embedding effect of different embedding strategies

the best embedding strategy is selected to obtain the sample vector. Then a convolutional neural networks model is constructed to predict adverse reactions. The results show that the convolutional neural networks model has the best prediction effect under the DistMult embedding model and the 400-dimensional embedding strategy. The mean values of accuracy

score

recall and Area Under Curve were 0.887

0.890

0.913 and 0.957

respectively

which are better than those reported in the literature. The prediction model has good prediction accuracy and stability

which can provide an effective reference for safe medication.

关键词

Keywords

references

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