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

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  • Francesco De Pretis

    (Department of Biomedical Sciences and Public Health, Marche Polytechnic University, 60126 Ancona, Italy
    Department of Communication and Economics, University of Modena and Reggio Emilia, 42121 Reggio Emilia, Italy
    Current address: Via Tronto 10/A, 60126 Ancona, Italy.
    These authors contributed equally to this work.)

  • Barbara Osimani

    (Department of Biomedical Sciences and Public Health, Marche Polytechnic University, 60126 Ancona, Italy
    Munich Center for Mathematical Philosophy, Ludwig-Maximilians-Universität München, 80539 München, Germany
    These authors contributed equally to this work.)

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.

Suggested Citation

  • Francesco De Pretis & Barbara Osimani, 2019. "New Insights in Computational Methods for Pharmacovigilance: E-Synthesis , a Bayesian Framework for Causal Assessment," IJERPH, MDPI, vol. 16(12), pages 1-19, June.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:12:p:2221-:d:242461
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    References listed on IDEAS

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    Cited by:

    1. Elena Rocca & Rani Lill Anjum, 2020. "Causal Evidence and Dispositions in Medicine and Public Health," IJERPH, MDPI, vol. 17(6), pages 1-18, March.

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