| アブストラクト | The use of multiple data sources has been preferred in the surveillance of adverse drug events due to shortcomings of using only a single source. In this study, we proposed a framework where the ADEs associated with interested drugs are systematically discovered from the FDA's Adverse Event Reporting System (AERS), and then validated through mining unstructured clinical notes from Electronic Medical Records (EMRs). This framework has two features. First, a higher priority was given to clinical practice during signal detection and validation. Second, the normalization by NLP facilitated the interoperation between AERS-DM and the EMR. To demonstrate this methodology, we investigated potential ADEs associated with drugs (class level) for rheumatoid arthritis (RA) patients. The results demonstrated the feasibility and sufficient accuracy of the framework. The framework can serve as the interface between the informatics domain and the medical domain to facilitate ADE discovery. |
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| 投稿日 | 2017/8/18 |
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| 投稿者 | Wang, Liwei; Rastegar-Mojarad, Majid; Liu, Sijia; Zhang, Huaji; Liu, Hongfang |
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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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| 組織名 | School of Public Health, Jilin University, Changchun, Jilin, China.;Department of Health Sciences Research, Mayo Clinic College of Medicine,;Rochester, MN, U.S.;Department of Computer Science and Engineering, University at Buffalo, Buffalo,;NY, U.S.;Chinese Pharmaceutical Association, Beijing, China. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/28815115/ |
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