| アブストラクト | Efficiently mining multiple drug interactions and reactions from Adverse Event Reporting System (AERS) is a challenging problem which has not been sufficiently addressed by existing methods. To tackle this challenge, we propose a FCI-fliter approach which leverages the efforts of UMLS mapping, frequent closed itemset mining, and uninformative association identification and removal. By applying our method on AERS, we identified a large number of multiple drug interactions with reactions. By statistical analysis, we found most of the identified associations have very small p-values which suggest that they are statistically significant. Further analysis on the results shows that many multiple drug interactions and reactions are clinically interesting, and suggests that our method may be further improved with the combination of external knowledge. |
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| ジャーナル名 | AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science |
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| Pubmed追加日 | 2015/2/27 |
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| 投稿者 | Xiang, Yang; Albin, Aaron; Ren, Kaiyu; Zhang, Pengyue; Etter, Jonathan P; Lin, Simon; Li, Lang |
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| 組織名 | Department of Biomedical Informatics, The Ohio State University, Columbus, OH;43210.;43210 ; Department of Computer Science and Engineering, The Ohio State;University, Columbus, OH 43210.;Center for Computational Biology and Bioinformatics, Indiana University,;Indianapolis, IN 46202.;Division of Medicinal Chemistry & Pharmacognosy, The Ohio State University,;Columbus, OH 43210.;Biomedical Informatics Research Center, Marshfield Clinic Research Foundation,;Marshfield, WI 54449. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/25717411/ |
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