Visualization of FDA Adverse Drug Reaction Reports: Development and Usability Study of the VisDrugs Web Server
Bibliographic record
Abstract
Background: Adverse drug reactions (ADRs) are a major concern in drug safety, and the FDA Adverse Event Reporting System (FAERS) provides valuable ADR data. However, analyzing FAERS data is complex and requires bioinformatics expertise. Despite the vast amount of ADR data available, there is a lack of user-friendly tools that enable efficient visualization and comparison of ADRs for researchers and health care professionals. Objective: This study aimed to develop VisDrugs, a web-based platform that simplifies ADR visualization and comparison using FAERS data. The platform was designed to assist researchers and clinicians in assessing drug safety through interactive and interpretable graphical representations of ADR patterns. Methods: FAERS data were extracted in the American Standard Code for Information Interchange (ASCII) format, covering the period from Q3 (third quarter) 2014 to Q3 2024. About 2,700,000 reports from health care professionals, where only a single drug was implicated, were aggregated and processed using R for statistical analysis and visualization. The results are presented on a web-based platform for web-based analysis. The platform generates pie charts to visualize the most frequently reported ADRs, which are represented and analyzed using preferred terms based on the Medical Dictionary for Regulatory Activities (MedDRA) and forest plots illustrating reporting odds ratios (RORs) for these ADRs. Results: Using Paxlovid (COVID-19 treatment) and hydroxychloroquine (anti-malaria drug) as case studies, we benchmarked VisDrugs using reports for Paxlovid (n=16,708) and hydroxychloroquine (n=6150). Paxlovid was most frequently associated with "COVID-19" (ROR=47.26, 95% CI 45.22-49.40) and "dysgeusia" (ROR=59.65, 95% CI 55.56-64.03). Hydroxychloroquine showed strong associations with "retinal toxicity" (ROR=738.48, 95% CI 583.45-934.71), "retinopathy" (ROR=412.27, 95% CI 344.73-493.03), and "cardiotoxicity" (ROR=48.36, 95% CI 38.86-60.19). In subgroup analyses, female patients had significantly higher risks of retinopathy (3.24-fold) and cardiomyopathy (13.82-fold) compared to male patients, while patients aged >50 years had higher risks of retinopathy (4.20-fold) and cardiomyopathy (7.84-fold) compared to those ≤50 years. All differences were statistically significant (z test, P<.01). The majority of findings align with existing research, thereby validating the platform's utility. Clinical personnel have evaluated and refined the platform based on user feedback, confirming its efficacy in visualizing complex ADR data and identifying adverse effects across various drug subgroups. Conclusions: VisDrugs is a valuable tool for ADR analysis, offering an intuitive interface for exploring FAERS data. By visualizing and comparing ADRs, it helps researchers and health care providers assess drug safety efficiently. The platform's demographic analysis features add insights into ADR variations by age and gender, supporting drug safety research. In the future, the website will include more subgroup or condition filtering options, offering personalized ADR analysis and comparison features to meet the diverse research needs of users.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".