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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Vydáno v:	BMC medical informatics and decision making Ročník 21; číslo 1; s. 38 - 11
Hlavní autoři:	Zhang, Fei, Sun, Bo, Diao, Xiaolin, Zhao, Wei, Shu, Ting
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 04.02.2021 BioMed Central Ltd Springer Nature B.V BMC
Témata:	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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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1472-6947 1472-6947
DOI:	10.1186/s12911-021-01402-3