Prediction of adverse drug reactions based on knowledge graph embedding
Abstract 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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2021-02-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-021-01402-3 |
| _version_ | 1818459726690648064 |
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| author | Fei Zhang Bo Sun Xiaolin Diao Wei Zhao Ting Shu |
| author_facet | Fei Zhang Bo Sun Xiaolin Diao Wei Zhao Ting Shu |
| author_sort | Fei Zhang |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-14T23:18:56Z |
| format | Article |
| id | doaj.art-e42ecf8b740649bbb1787de4829aca06 |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-12-14T23:18:56Z |
| publishDate | 2021-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-e42ecf8b740649bbb1787de4829aca062022-12-21T22:44:01ZengBMCBMC Medical Informatics and Decision Making1472-69472021-02-0121111110.1186/s12911-021-01402-3Prediction of adverse drug reactions based on knowledge graph embeddingFei Zhang0Bo Sun1Xiaolin Diao2Wei Zhao3Ting Shu4Department of Information Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical CollegeDepartment of Information Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical CollegeDepartment of Information Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical CollegeDepartment of Information Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical CollegeNational Institute of Hospital Administration, National Health CommissionAbstract 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.https://doi.org/10.1186/s12911-021-01402-3Adverse Drug ReactionsKnowledge Graph EmbeddingWord2VecDrugBank |
| spellingShingle | Fei Zhang Bo Sun Xiaolin Diao Wei Zhao Ting Shu Prediction of adverse drug reactions based on knowledge graph embedding BMC Medical Informatics and Decision Making Adverse Drug Reactions Knowledge Graph Embedding Word2Vec DrugBank |
| title | Prediction of adverse drug reactions based on knowledge graph embedding |
| title_full | Prediction of adverse drug reactions based on knowledge graph embedding |
| title_fullStr | Prediction of adverse drug reactions based on knowledge graph embedding |
| title_full_unstemmed | Prediction of adverse drug reactions based on knowledge graph embedding |
| title_short | Prediction of adverse drug reactions based on knowledge graph embedding |
| title_sort | prediction of adverse drug reactions based on knowledge graph embedding |
| topic | Adverse Drug Reactions Knowledge Graph Embedding Word2Vec DrugBank |
| url | https://doi.org/10.1186/s12911-021-01402-3 |
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