New Insights in Computational Methods for Pharmacovigilance: E-Synthesis, a Bayesian Framework for Causal Assessment.

• 影响指数: 4.614
• 发表时间: 06 2019 24
• 来源期刊: Int J Environ Res Public Health
• DOI: 10.3390/ijerph16122221
• PMID: 31238543
• 文章类型: Journal Article
• 作者列表: De Pretis F,Osimani B

关键词: Bayesian epistemology; E-Synthesis; advisory committees; data fusion; data mining; dose-responsiveness; drug approval process; evidence synthesis; pharmacovigilance

Mesh: Animals; Bayes Theorem; Data Mining; Drug-Related Side Effects and Adverse Reactions; Humans; Pharmacovigilance

Abstract

Today\'s surge of big data coming from multiple sources is raising the stakes that pharmacovigilance has to win, making evidence synthesis a more and more robust approach in the field. In this scenario, many scholars believe that new computational methods derived from data mining will effectively enhance the detection of early warning signals for adverse drug reactions, solving the gauntlets that post-marketing surveillance requires. This article highlights the need for a philosophical approach in order to fully realize a pharmacovigilance 2.0 revolution. A state of the art on evidence synthesis is presented, followed by the illustration of E-Synthesis, a Bayesian framework for causal assessment. Computational results regarding dose-response evidence are shown at the end of this article.