ArticleJournal of medical Internet research2021
Quantifying the Severity of Adverse Drug Reactions Using Social Media: Network Analysis.
Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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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
10 citing papers in PubMed.
- Computational Prediction of the Severity of Adverse Drug Reactions Caused by Drug-Drug Interactions.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Review
- Augmenting fact and date of death in electronic health records using internet media sources: a validation study from two large healthcare systems.American journal of epidemiology · 2026Article
- Detecting Adverse Drug Events in Social Media: A Brief Literature Review.SN computer science · 2026Article
- Disparities and trends in drug-related adverse reaction mortality in the United States, 1999-2020.Therapeutic advances in drug safety · 2026Article
- DrIVeNN: Drug Interaction Vectors Neural Network.Journal of computational biology : a journal of computational molecular cell biology · 2025Article
- Monitoring the opioid epidemic via social media discussions.NPJ digital medicine · 2025Article
- Pharmacovigilance in the digital age: gaining insight from social media data.Experimental biology and medicine (Maywood, N.J.) · 2025Review
- Mining Real-World Big Data to Characterize Adverse Drug Reaction Quantitatively: Mixed Methods Study.Journal of medical Internet research · 2024Article
- Using GPT-3 to Build a Lexicon of Drugs of Abuse Synonyms for Social Media Pharmacovigilance.Biomolecules · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundAdverse drug reactions (ADRs) affect the health of hundreds of thousands of individuals annually in the United States, with associated costs of hundreds of billions of dollars. The monitoring and analysis of the severity of ADRs is limited by the current qualitative and categorical systems of severity classification. Previous efforts have generated quantitative estimates for a subset of ADRs but were limited in scope because of the time and costs associated with the efforts.
objectiveThe aim of this study is to increase the number of ADRs for which there are quantitative severity estimates while improving the quality of these severity estimates.
methodsWe present a semisupervised approach that estimates ADR severity by using social media word embeddings to construct a lexical network of ADRs and perform label propagation. We used this method to estimate the severity of 28,113 ADRs, representing 12,198 unique ADR concepts from the Medical Dictionary for Regulatory Activities.
resultsOur Severity of Adverse Events Derived from Reddit (SAEDR) scores have good correlations with real-world outcomes. The SAEDR scores had Spearman correlations of 0.595, 0.633, and -0.748 for death, serious outcome, and no outcome, respectively, with ADR case outcomes in the Food and Drug Administration Adverse Event Reporting System. We investigated different methods for defining initial seed term sets and evaluated their impact on the severity estimates. We analyzed severity distributions for ADRs based on their appearance in boxed warning drug label sections, as well as for ADRs with sex-specific associations. We found that ADRs discovered in the postmarketing period had significantly greater severity than those discovered during the clinical trial (P<.001). We created quantitative drug-risk profile (DRIP) scores for 968 drugs that had a Spearman correlation of 0.377 with drugs ranked by the Food and Drug Administration Adverse Event Reporting System cases resulting in death, where the given drug was the primary suspect.
conclusionsOur SAEDR and DRIP scores are well correlated with the real-world outcomes of the entities they represent and have demonstrated utility in pharmacovigilance research. We make the SAEDR scores for 12,198 ADRs and the DRIP scores for 968 drugs publicly available to enable more quantitative analysis of pharmacovigilance data.
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