Cargando…
Efficiently mining Adverse Event Reporting System for multiple drug interactions
Efficiently mining multiple drug interactions and reactions from Adverse Event Reporting System (AERS) is a challenging problem which has not been sufficiently addressed by existing methods. To tackle this challenge, we propose a FCI-fliter approach which leverages the efforts of UMLS mapping, frequ...
Autores principales: | Xiang, Yang, Albin, Aaron, Ren, Kaiyu, Zhang, Pengyue, Etter, Jonathan P., Lin, Simon, Li, Lang |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Medical Informatics Association
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4333704/ https://www.ncbi.nlm.nih.gov/pubmed/25717411 |
Ejemplares similares
-
Propensity score‐adjusted three‐component mixture model for drug‐drug interaction data mining in FDA Adverse Event Reporting System
por: Wang, Xueying, et al.
Publicado: (2019) -
Mining reported adverse events induced by potential opioid-drug interactions
por: Chen, Jinzhao, et al.
Publicado: (2020) -
Three‐Component Mixture Model‐Based Adverse Drug Event Signal Detection for the Adverse Event Reporting System
por: Zhang, Pengyue, et al.
Publicado: (2018) -
Mining and visualizing high-order directional drug interaction effects using the FAERS database
por: Yao, Xiaohui, et al.
Publicado: (2020) -
Mining severe drug-drug interaction adverse events using Semantic Web technologies: a case study
por: Jiang, Guoqian, et al.
Publicado: (2015)