Prediction of adverse drug reactions based on knowledge graph embedding

Background Adverse drug reactions (ADRs) are an important concern in the medication process and can pose a substantial economic burden for patients and hospitals. Because of the limitations of clinical trials, it is difficult to identify all possible ADRs of a drug before it is marketed. We develope...

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Bibliographic Details
Published in:BMC medical informatics and decision making Vol. 21; no. 1; pp. 38 - 11
Main Authors: Zhang, Fei, Sun, Bo, Diao, Xiaolin, Zhao, Wei, Shu, Ting
Format: Journal Article
Language:English
Published: London BioMed Central 04.02.2021
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
Adverse and side effects
Adverse Drug Reactions
Classification
Clinical trials
Computational linguistics
Data Mining
Dictionaries
Drug-Related Side Effects and Adverse Reactions - epidemiology
DrugBank
Drugs
Embedding
Health Informatics
Humans
Information Systems and Communication Service
Knowledge acquisition (Expert systems)
Knowledge Graph Embedding
Knowledge representation
Language processing
Logistic Models
Machine learning
Management
Management of Computing and Information Systems
Medicine
Medicine & Public Health
Methods
Model accuracy
modeling
Natural language interfaces
Natural Language Processing
Pattern Recognition, Automated
Predictions
Proteins
R&D
Regression models
Research & development
Research Article
Semantics
Side effects
technology
Word2Vec
Timor-Leste
United States > US
Knowledge Graph Embedding
Adverse Drug Reactions
DrugBank
Word2Vec
ISSN:1472-6947, 1472-6947
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Summary:Background Adverse drug reactions (ADRs) are an important concern in the medication process and can pose a substantial economic burden for patients and hospitals. Because of the limitations of clinical trials, it is difficult to identify all possible ADRs of a drug before it is marketed. We developed a new model based on data mining technology to predict potential ADRs based on available drug data. Method Based on the Word2Vec model in Nature Language Processing, we propose a new knowledge graph embedding method that embeds drugs and ADRs into their respective vectors and builds a logistic regression classification model to predict whether a given drug will have ADRs. Result First, a new knowledge graph embedding method was proposed, and comparison with similar studies showed that our model not only had high prediction accuracy but also was simpler in model structure. In our experiments, the AUC of the classification model reached a maximum of 0.87, and the mean AUC was 0.863. Conclusion In this paper, we introduce a new method to embed knowledge graph to vectorize drugs and ADRs, then use a logistic regression classification model to predict whether there is a causal relationship between them. The experiment showed that the use of knowledge graph embedding can effectively encode drugs and ADRs. And the proposed ADRs prediction system is also very effective.
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ISSN:1472-6947
1472-6947
DOI:10.1186/s12911-021-01402-3