基于知识图谱嵌入与深度学习的药物不良反应预测

doi:10.12052/gdutxb.230031

摘要/Abstract

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、F₁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。

关键词: 药物不良反应, 知识图谱嵌入, 深度学习, 预测模型

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 and 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, F₁ 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.

Key words: adverse drug reaction, knowledge graph embedding, deep learning, prediction model

中图分类号:

TP399

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(01): 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]. Journal of Guangdong University of Technology, 2024, 41(01): 19-26,40.

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