基于知识图谱嵌入与深度学习的药物不良反应预测

吴菊华; 李俊锋; 陶雷

doi:10.12052/gdutxb.230031

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

基于知识图谱嵌入与深度学习的药物不良反应预测

智慧医疗 | 更新时间：2026-05-27

- 基于知识图谱嵌入与深度学习的药物不良反应预测
- Prediction of Adverse Drug Reactions Based on Knowledge Graph Embedding and Deep Learning
- 广东工业大学学报 2024年41卷第1期页码：19-26, 40
- 作者机构：
  
  广东工业大学管理学院,广东,广州,510520
- 作者简介：
  
  吴菊华(1974–) ，女，教授，主要研究方向为智慧健康管理、商务智能和工业互联网等
  李俊锋(1995–) ，男，硕士研究生，主要研究方向为数据挖掘、机器学习，E-mail：751947938@qq.com
- 基金信息：
  
  国家自然科学基金资助面上项目(71771059);广东省基础与应用基础研究基金资助项目(2021A1515220031)
- DOI：10.12052/gdutxb.230031
  中图分类号： TP399
- 网络首发：2024-01-25，
  
  纸质出版：2024
- 稿件说明：
移动端阅览
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

WESTER K, JONSSON A K, SPIGSET O, et al . Incidence of fatal adverse drug reactions: a population based study [J ] . British Journal of Clinical Pharmacology, 2008, 65(4): 573-579.

COCOS A, FIKS A G, MASINO A J. Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts [J]. Journal of the American Medical Informatics Association, 2017, 24(4): 813-821.

JONATHAN H W, TERRY M, JAN D H. Cost of prescription drug-related morbidity and mortality [J]. Annals of Pharmacotherapy, 2018, 52(9): 829-837.

ZHU X X, YANG Z Q, LIU J. Construction of an adverse drug reaction extraction model based on Bi-LSTM and CRF [J]. Data Analysis and Knowledge Discovery, 2019, 3(2): 90-97.

国家药品不良反应监测年度报告(2018年) [J]. 中国药物评价, 2019, 36(6) : 476-480

郭凯. 基于深度学习和语义分析的药物不良反应发现[D]. 大连: 大连理工大学, 2017.

朱嘉静. 基于机器学习的药物不良反应关键问题研究[D]. 成都: 电子科技大学, 2020.

LEE C Y, CHEN Y P. Machine learning on adverse drug reactions for pharmacovigilance [J]. Drug Discovery Today, 2019, 24(7): 1332-1343.

LEE C Y, CHEN Y P. Prediction of drug adverse events using deep learning in pharmaceutical discovery [J]. Briefings in Bioinformatics, 2020, 22(2): 1884-1901.

World Health Organization. International drug monitoring, the role of national centres: report of WHO Technical Group[R]. Geneva: WHO, 1972.

RING J, BROCKOW K. Adverse drug reactions: mechanisms and assessment [J]. European Surgical Research, 2002, 34(1-2): 170-175.

TAN Z Y, ZHAO R S. Progress of studies of artificial intelligence in surveillance and report of adverse drug reactions [J]. Clinical Medication Journal, 2019, 17(2): 23-27.

ZHAO X, CHEN Y, LIAO J, et al . Signal mining for adverse drug reactions based on healthcare big data: methodology and applications [J ] . Chinese Journal of Hospital Administration, 2017, 33(5): 4.

VOSS E A, BOYCE RD, RYAN P B, et al . Accuracy of an automated knowledge base for identifying drug adverse reactions [J ] . Journal of Biomedical Informatics, 2017, 66: 72-81.

CHEN Y, WU H, GE W H, et al . Research on entity relation extraction of Chinese adverse drug reaction reports based on deep learning method [J ] . Journal of China Pharmaceutical University, 2019, 50(6): 753-759.

SHEN C, LIN H F. Detection method of adverse drug events from social media based on graph embedding [J]. Journal of Dalian University of Technology, 2020, 60(5): 547-554.

ZHONG Y L, MA S W, LU H J, et al . Survey of research on entity recognition of adverse drug reaction based on machine learning [J ] . Software Engineering, 2022, 25(8): 1-6.

LIU M, WU Y H, CHEN Y K, et al . Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs [J ] . Journal of the American Medical Informatics Association, 2012, 19(e1): e28-35.

VILAR S, HRIPCSAK G. The role of drug profiles as similarity metrics: applications to repurposing, adverse effects detection and drug-drug interactions [J]. Briefings in Bioinformatics, 2017, 18(4): 670-681.

MUNOZ E, NOVACEK V, VANDENBUSSCHE P Y. Using drug similarities for discovery of possible adverse reactions[J]. Amia Annual Symposium Proceedings, 2016, 2016: 924-933

WANG C S, LIN P J, CHENG C L, et al . Detecting potential adverse drug reactions using a deep neural network model [J ] . Journal of Medical Internet Research, 2019, 21(2): e11016.

DAI Y F, WANG S P, NEAL N, et al . A survey on knowledge graph embedding: approaches, applications and benchmarks [J ] . Electronics, 2020, 9(5): 750-778.

ZENG X X, TU X Q, LIU Y S, et al. Toward better drug discovery with knowledge graph[J]. Current Opinion in Structural Biology, 2022, 72: 114-126

SHTAR G, ROKACH L, SHAPIRA B. Detecting drug-drug interactions using artificial neural networks and classic graph similarity measures [J]. PloS One, 2019, 14(8): e0219796.

JOSHI P, MASILAMANI V, MUKHERJEE A. A knowledge graph embedding based approach to predict the adverse drug reactions using a deep neural network [J]. Journal of Biomedical Informatics, 2022, 132: 104122-104131.

ZHANG F, SUN B, DIAO X L, et al . Prediction of adverse drug reactions based on knowledge graph embedding [J ] . BMC Medical Informatics and Decision Making, 2021, 21(1): 38-48.

WANG M, MA X Y, SI J W, et al. Adverse drug reaction discovery using a tumor-biomarker knowledge graph[J]. Frontiers in Genetics, 2021, 11: 625659

DEY S, LUO H, FOKOUE A, et al . Predicting adverse drug reactions through interpretable deep learning framework [J ] . BMC Bioinformatics, 2018, 19(Suppl 21): 476-488.

BEAN D M, WU H H, IQBAL E, et al . Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records [J ] . Scientific Reports, 2017, 7(1): 16416.

WISHART D S, FEUNANG Y D, GUO A C, et al . DrugBank 5.0: a major update to the DrugBank database for 2018 [J ] . Nucleic Acids Research, 2018, 46(D1): D1074-D1082.

KUHN M, LETUNIC I, JENSEN L J, et al . The SIDER database of drugs and side effects [J ] . Nucleic Acids Research, 2016, 44(D1): D1075-D1084.

NICKEL M, LORENZO R, TOMASO P. Holographic embeddings of knowledge graphs[C] //Thirtieth AAAI Conference on Artificial Intelligence. California: AAAI. 2016: 1955-1961.

GAO F M, SONG Z H. Kidney injury caused by esomeprazole magnesium enteric-coated tablets [J]. Adverse Drug Reactions Journal, 2020, 22(6): 381-382.

DHAKRIT R, IVES A L, HARRIOTT N G, et al . Comparative incidence of acute kidney injury in patients on vancomycin therapy in combination with cefepime, piperacillin-tazobactam or meropenem [J ] . Journal of Chemotherapy (Florence, Italy) , 2021, 34(2): 1-7.

NIITSU T, HAYASHI T, UCHIDA J, et al . Drug-induced kidney injury caused by osimertinib: report of a rare case [J ] . Nephron, 2021, 146(1): 58-63.

WAKABAYASHI K, YAMAMOTO S, HARA S, et al . Nivolumab-induced membranous nephropathy in a patient with stage IV lung adenocarcinoma [J ] . CEN Case Rep, 2022, 11(2): 171-176.

SU W, SU P. Effect of adverse reaction of acquired acute renal failure caused by iohexol injection [J]. Chinese Journal of Biochemical and Pharmaceuticals, 2016, 36(10): 92-94.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

面向电力场景图像的动态自适应增量目标检测DA-IOD算法

基于域不变判别特征学习的深子域自适应方法

基于主动学习和视觉状态空间模型的热点检测器

双模态迭代交叉注意力融合集成框架

基于路况参数的混合动力机车能量管理策略研究