Cargando…
New Insights in Computational Methods for Pharmacovigilance: E-Synthesis, a Bayesian Framework for Causal Assessment
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 enh...
Autores principales: | De Pretis, Francesco, Osimani, Barbara |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6617215/ https://www.ncbi.nlm.nih.gov/pubmed/31238543 http://dx.doi.org/10.3390/ijerph16122221 |
Ejemplares similares
-
E-Synthesis: A Bayesian Framework for Causal Assessment in Pharmacosurveillance
por: De Pretis, Francesco, et al.
Publicado: (2019) -
InferBERT: A Transformer-Based Causal Inference Framework for Enhancing Pharmacovigilance
por: Wang, Xingqiao, et al.
Publicado: (2021) -
Evaluation of inter-rater agreement between three causality assessment methods used in pharmacovigilance
por: Thaker, Saket J., et al.
Publicado: (2016) -
Dispositions and Causality Assessment in Pharmacovigilance: Proposing the Dx3 Approach for Assessing Causality with Small Data Sets
por: Anjum, Rani Lill, et al.
Publicado: (2022) -
Incentives for Research Effort: An Evolutionary Model of Publication Markets with Double-Blind and Open Review
por: Radzvilas, Mantas, et al.
Publicado: (2022)