ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2014
Efficiently mining Adverse Event Reporting System for multiple drug interactions.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Application of an Innovative Data Mining Approach Towards Safe Polypharmacy Practice in Older Adults.Drug safety · 2024Article
- Prevalence of potentially harmful multidrug interactions on medication lists of elderly ambulatory patients.BMC geriatrics · 2021Article
- Detection of drug-drug interactions through data mining studies using clinical sources, scientific literature and social media.Briefings in bioinformatics · 2018Review
- Mixture drug-count response model for the high-dimensional drug combinatory effect on myopathy.Statistics in medicine · 2018Article
- Translational Biomedical Informatics and Pharmacometrics Approaches in the Drug Interactions Research.CPT: pharmacometrics & systems pharmacology · 2018Review
- The potential of translational bioinformatics approaches for pharmacology research.British journal of clinical pharmacology · 2015Article
- A Mixture Dose-Response Model for Identifying High-Dimensional Drug Interaction Effects on Myopathy Using Electronic Medical Record Databases.CPT: pharmacometrics & systems pharmacology · 2015Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
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, frequent closed itemset mining, and uninformative association identification and removal. By applying our method on AERS, we identified a large number of multiple drug interactions with reactions. By statistical analysis, we found most of the identified associations have very small p-values which suggest that they are statistically significant. Further analysis on the results shows that many multiple drug interactions and reactions are clinically interesting, and suggests that our method may be further improved with the combination of external knowledge.
Identifiers
25717411PMC4333704What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.