aer2vec: Distributed Representations of Adverse Event Reporting System Data as a Means to Identify Drug/Side-Effect Associations
| Autor: | Nathan Murray, Jake Portanova, Devika Subramanian, Justin Mower, Trevor Cohen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Computer science business.industry SIGNAL (programming language) Articles Machine learning computer.software_genre 030226 pharmacology & pharmacy Causality 03 medical and health sciences Adverse Event Reporting System 0302 clinical medicine Resource (project management) Key (cryptography) 030212 general & internal medicine Artificial intelligence Adverse effect business Feature learning computer |
| Zdroj: | Scopus-Elsevier |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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