| アブストラクト | Adverse event report (AER) data are a key source of signal for post marketing drug surveillance. The standard methodology to analyze AER data applies disproportionality metrics, which estimate the strength of drug/side-effect associations from discrete counts of their occurrence at report level. However, in other domains, improvements in predictive modeling accuracy have been obtained through representation learning, where discrete features are replaced by distributed representations learned from unlabeled data. This paper describes aer2vec, a novel representational approach for AER data in which concept embeddings emerge from neural networks trained to predict drug/side-effect co-occurrence. Trained models are evaluated for their utility in identifying drug/side-effect relationships, with improvements over disproportionality metrics in most cases. In addition, we evaluate the utility of an otherwise-untapped resource in the Food and Drug Administration (FDA) AER system - reporter designations of suspected causality - and find that incorporating this information enhances performance of all models evaluated. |
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| ジャーナル名 | AMIA ... Annual Symposium proceedings. AMIA Symposium |
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| Pubmed追加日 | 2020/4/21 |
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| 投稿者 | Portanova, Jake; Murray, Nathan; Mower, Justin; Subramanian, Devika; Cohen, Trevor |
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| 組織名 | University of Washington, Seattle, WA.;Hofstra University, East Garden City, New York.;Rice University, Houston, TX. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/32308867/ |
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