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• W2922082445 endingPage "1343" @default.
• W2922082445 startingPage "1332" @default.
• W2922082445 abstract "• A systematic review on adverse drug reactions (ADRs). • The application of machine learning in pharmacovigilance. • A framework to predict ADRs after the post-marketing of drugs. • Identify unknown compound combinations. • Adequately capture more information on drug substructures. Adverse drug reactions are an unresolved issue that can result in mortality, morbidity and substantial healthcare costs. Many conventional machine learning methods have been used for predicting post-marketing drug side-effects. However, owing to the complex chemical structures of certain drugs and the nonlinear and imbalanced nature of biological data, some side-effects might not be detected. Motivated by the drug discovery research studies that have shown that deep learning outperformed machine learning methods over prediction tasks, we proposed: (i) to exploit the unsupervised deep learning approaches to predict ADRs; (ii) to use a two-stage framework to predict personalized ADRs and repurpose the drugs. This work demonstrates that the proposed framework shows promise in providing more-accurate prediction of side-effects and drug repurposing. Machine learning, especially deep learning, has the predictive power to predict adverse drug reactions, repurpose drugs and perform precision medicine. We provide a background of machine learning and propose a potential high-performance deep learning framework for its successful applications in these practices." @default.
• W2922082445 created "2019-03-22" @default.
• W2922082445 creator A5036915271 @default.
• W2922082445 creator A5063038462 @default.
• W2922082445 date "2019-07-01" @default.
• W2922082445 modified "2023-10-18" @default.
• W2922082445 title "Machine learning on adverse drug reactions for pharmacovigilance" @default.
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• W2922082445 doi "https://doi.org/10.1016/j.drudis.2019.03.003" @default.
• W2922082445 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30876845" @default.
• W2922082445 hasPublicationYear "2019" @default.
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