ArticleJournal of the American Medical Informatics Association : JAMIA
A novel signal detection algorithm for identifying hidden drug-drug interactions in adverse event reports.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers.
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Who cites it
90 citing papers in PubMed.
- The Pharmacokinetic Interaction Between Metformin and the Natural Product Goldenseal Is Metformin Dose-Dependent: A Three-Arm Crossover Study in Adults With Type 2 Diabetes.Clinical and translational science · 2025Trial
- Prospective adverse event risk evaluation in clinical trials.Health care management science · 2022Trial
- Polypharmacy and Drug-Drug Interaction Architecture in Hospitalized Cardiovascular Patients: Insights from Real-World Analysis.Biomedicines · 2026Article
- Research on drug-drug interaction prediction using capsule neural network based on self-attention mechanism.BMC bioinformatics · 2025Article
- A substructure-aware graph neural network incorporating relation features for drug-drug interaction prediction.Quantitative biology (Beijing, China) · 2024Article
- Prevalence and differences in the co-administration of drugs known to interact: an analysis of three distinct and large populations.BMC medicine · 2024Article
- SubGE-DDI: A new prediction model for drug-drug interaction established through biomedical texts and drug-pairs knowledge subgraph enhancement.PLoS computational biology · 2024Article
- Article
- Active monitoring of antifungal adverse events in hospitalized patients based on Global Trigger Tool method.Frontiers in pharmacology · 2024Article
- A novel efficient drug repurposing framework through drug-disease association data integration using convolutional neural networks.BMC bioinformatics · 2023Article
- Multi-view feature representation and fusion for drug-drug interactions prediction.BMC bioinformatics · 2023Article
- MDDI-SCL: predicting multi-type drug-drug interactions via supervised contrastive learning.Journal of cheminformatics · 2022Article
- A network paradigm predicts drug synergistic effects using downstream protein-protein interactions.CPT: pharmacometrics & systems pharmacology · 2022Article
- Computational systems biology in disease modeling and control, review and perspectives.NPJ systems biology and applications · 2022Review
- Predicting Drug Blood-Brain Barrier Penetration with Adverse Event Report Embeddings.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Advancement in predicting interactions between drugs used to treat psoriasis and its comorbidities by integrating molecular and clinical resources.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Novel deep learning-based transcriptome data analysis for drug-drug interaction prediction with an application in diabetes.BMC bioinformatics · 2021Article
- Machine learning approaches and databases for prediction of drug-target interaction: a survey paper.Briefings in bioinformatics · 2021Review
- A Review of Approaches for Predicting Drug-Drug Interactions Based on Machine Learning.Frontiers in pharmacology · 2021Review
- Prediction of the Drug-Drug Interaction Types with the Unified Embedding Features from Drug Similarity Networks.Frontiers in pharmacology · 2021Article
30 more citing papers are in PubMed but not listed here.
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3 authors.
Funding
Abstract
objectiveAdverse drug events (ADEs) are common and account for 770 000 injuries and deaths each year and drug interactions account for as much as 30% of these ADEs. Spontaneous reporting systems routinely collect ADEs from patients on complex combinations of medications and provide an opportunity to discover unexpected drug interactions. Unfortunately, current algorithms for such "signal detection" are limited by underreporting of interactions that are not expected. We present a novel method to identify latent drug interaction signals in the case of underreporting. MATERIALS AND
methodsWe identified eight clinically significant adverse events. We used the FDA's Adverse Event Reporting System to build profiles for these adverse events based on the side effects of drugs known to produce them. We then looked for pairs of drugs that match these single-drug profiles in order to predict potential interactions. We evaluated these interactions in two independent data sets and also through a retrospective analysis of the Stanford Hospital electronic medical records.
resultsWe identified 171 novel drug interactions (for eight adverse event categories) that are significantly enriched for known drug interactions (p=0.0009) and used the electronic medical record for independently testing drug interaction hypotheses using multivariate statistical models with covariates.
conclusionOur method provides an option for detecting hidden interactions in spontaneous reporting systems by using side effect profiles to infer the presence of unreported adverse events.
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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.