Abstract
Adverse drug reactions (ADRs) may occur following a single dose or prolonged administration of a drug or result from the combination of two or more drugs. Given the restrictions of the traditional methods like clinical trials, it’s difficult to detect the ADRs in a timely manner. Many countries have built spontaneous adverse drug event reporting systems, which provide a large amount of adverse drug event reports for research purpose. In this paper, we utilize the association rule mining to reconstruct the data from adverse drug event reports, and apply modified embedding models to calculate the relevance of the drug and adverse reactions to detect potential ADRs. We examine the effectiveness of methods by conducting experiments on two drugs: Gadoversetamide and Rofecoxib, finding 6 potential drug reactions, which can be further verified by biomedical data.
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Acknowledgements
This work is partially supported by grant from the Natural Science Foundation of China (Nos. 61572102, 61402075, 61602078, 61562080), the Fundamental Research Funds for the Central Universities the National Key Research Development Program of China (No. 2016YFB1001103).
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Guo, K. et al. (2017). Detecting Potential Adverse Drug Reactions Using Association Rules and Embedding Models. In: Cai, Z., Daescu, O., Li, M. (eds) Bioinformatics Research and Applications. ISBRA 2017. Lecture Notes in Computer Science(), vol 10330. Springer, Cham. https://doi.org/10.1007/978-3-319-59575-7_37
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DOI: https://doi.org/10.1007/978-3-319-59575-7_37
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