ArticleScientific reports2018
An MCEM Framework for Drug Safety Signal Detection and Combination from Heterogeneous Real World Evidence.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Using Artificial Intelligence to Identify Patterns and Predictors of Adverse Drug Reactions in Cardiovascular Patients: A Comprehensive Narrative Review.American journal of cardiovascular drugs : drugs, devices, and other interventions · 2026Review
- Design Principles for Interactive Dashboards in Drug Safety Surveillance: Design Science Research.JMIR medical informatics · 2026Article
- Ranking the Diabetes-related Safety Profile of Different Statin Drugs.Current pharmaceutical design · 2026Article
- Mining and analysis of drug-induced shock adverse reactions: A comprehensive pharmacovigilance study based on the US FAERS database.PloS one · 2025Article
- Article
- Review
- A New Drug Safety Signal Detection and Triage System Integrating Sequence Symmetry Analysis and Tree-Based Scan Statistics with Longitudinal Data.Clinical epidemiology · 2023Article
- iADRGSE: A Graph-Embedding and Self-Attention Encoding for Identifying Adverse Drug Reaction in the Earlier Phase of Drug Development.International journal of molecular sciences · 2022Article
- Identifying new drugs associated with pulmonary arterial hypertension: A WHO pharmacovigilance database disproportionality analysis.British journal of clinical pharmacology · 2022Article
- Systematic analysis of drug-associated myocarditis reported in the World Health Organization pharmacovigilance database.Nature communications · 2022Article
- Combining Social Media and FDA Adverse Event Reporting System to Detect Adverse Drug Reactions.Drug safety · 2020Article
- A Comparison Study of Algorithms to Detect Drug-Adverse Event Associations: Frequentist, Bayesian, and Machine-Learning Approaches.Drug safety · 2019Article
- Developing a Data-driven Medication Indication Knowledge Base using a Large Scale Medical Claims Database.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- Predicting adverse drug reactions through interpretable deep learning framework.BMC bioinformatics · 2018Article
- First Conference on Big Data for Pharmacovigilance.Drug safety · 2018Review
- Analysis of a pharmacist-led adverse drug event management model for pharmacovigilance in an academic medical center hospital in China.Therapeutics and clinical risk management · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Delayed drug safety insights can impact patients, pharmaceutical companies, and the whole society. Post-market drug safety surveillance plays a critical role in providing drug safety insights, where real world evidence such as spontaneous reporting systems (SRS) and a series of disproportional analysis serve as a cornerstone of proactive and predictive drug safety surveillance. However, they still face several challenges including concomitant drugs confounders, rare adverse drug reaction (ADR) detection, data bias, and the under-reporting issue. In this paper, we are developing a new framework that detects improved drug safety signals from multiple data sources via Monte Carlo Expectation-Maximization (MCEM) and signal combination. In MCEM procedure, we propose a new sampling approach to generate more accurate SRS signals for each ADR through iteratively down-weighting their associations with irrelevant drugs in case reports. While in signal combination step, we adopt Bayesian hierarchical model and propose a new summary statistic such that SRS signals can be combined with signals derived from other observational health data allowing for related signals to borrow statistical support with adjustment of data reliability. They combined effectively alleviate the concomitant confounders, data bias, rare ADR and under-reporting issues. Experimental results demonstrated the effectiveness and usefulness of the proposed framework.
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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.