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Prediction of adverse drug reactions based on knowledge graph embedding.
Zhang, Fei; Sun, Bo; Diao, Xiaolin; Zhao, Wei; Shu, Ting.
Afiliação
  • Zhang F; Department of Information Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 167 North Lishi Road, Xicheng District, Beijing, 100037, China.
  • Sun B; Department of Information Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 167 North Lishi Road, Xicheng District, Beijing, 100037, China.
  • Diao X; Department of Information Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 167 North Lishi Road, Xicheng District, Beijing, 100037, China.
  • Zhao W; Department of Information Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 167 North Lishi Road, Xicheng District, Beijing, 100037, China. zw@fuwai.com.
  • Shu T; National Institute of Hospital Administration, National Health Commission, Building 3, Yard 6, Shouti South Road, Haidian, Beijing, 100044, China. nctingting@126.com.
BMC Med Inform Decis Mak ; 21(1): 38, 2021 02 04.
Article em En | MEDLINE | ID: mdl-33541342
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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Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Reconhecimento Automatizado de Padrão / Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: BMC Med Inform Decis Mak Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Reconhecimento Automatizado de Padrão / Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: BMC Med Inform Decis Mak Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: China