ArticleJournal of biomedical informatics2019
RedMed: Extending drug lexicons for social media applications.
Article in Journal of biomedical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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The trial behind it
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Who cites it
15 citing papers in PubMed.
- I-NOWJ: adaptive context selection and knowledge-guided learning for substance use named entity recognition.Database : the journal of biological databases and curation · 2026Article
- Detecting Adverse Drug Events in Social Media: A Brief Literature Review.SN computer science · 2026Article
- Monitoring the opioid epidemic via social media discussions.NPJ digital medicine · 2025Article
- Open challenges and opportunities in federated foundation models towards biomedical healthcare.BioData mining · 2025Review
- Discovering opioid slang on social media: A Word2Vec approach with reddit data.Drug and alcohol dependence reports · 2024Article
- Alerts and Collections for Automating Patients' Sensemaking and Organizing of Their Electronic Health Record Data for Reflection, Planning, and Clinical Visits: Qualitative Research-Through-Design Study.JMIR human factors · 2023Article
- Using GPT-3 to Build a Lexicon of Drugs of Abuse Synonyms for Social Media Pharmacovigilance.Biomolecules · 2023Article
- A framework for multi-faceted content analysis of social media chatter regarding non-medical use of prescription medications.BMC digital health · 2023Article
- A linguistic analysis of dehumanization toward substance use across three decades of news articles.Frontiers in public health · 2023Article
- Language-agnostic pharmacovigilant text mining to elicit side effects from clinical notes and hospital medication records.Basic & clinical pharmacology & toxicology · 2022Article
- Using weak supervision to generate training datasets from social media data: a proof of concept to identify drug mentions.Neural computing & applications · 2021Article
- Quantifying the Severity of Adverse Drug Reactions Using Social Media: Network Analysis.Journal of medical Internet research · 2021Article
- A New Era in Pharmacovigilance: Toward Real-World Data and Digital Monitoring.Clinical pharmacology and therapeutics · 2021Review
- A Year of Papers Using Biomedical Texts.Yearbook of medical informatics · 2020Review
- Mining Social Media Data for Biomedical Signals and Health-Related Behavior.Annual review of biomedical data science · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Social media has been identified as a promising potential source of information for pharmacovigilance. The adoption of social media data has been hindered by the massive and noisy nature of the data. Initial attempts to use social media data have relied on exact text matches to drugs of interest, and therefore suffer from the gap between formal drug lexicons and the informal nature of social media. The Reddit comment archive represents an ideal corpus for bridging this gap. We trained a word embedding model, RedMed, to facilitate the identification and retrieval of health entities from Reddit data. We compare the performance of our model trained on a consumer-generated corpus against publicly available models trained on expert-generated corpora. Our automated classification pipeline achieves an accuracy of 0.88 and a specificity of >0.9 across four different term classes. Of all drug mentions, an average of 79% (±0.5%) were exact matches to a generic or trademark drug name, 14% (±0.5%) were misspellings, 6.4% (±0.3%) were synonyms, and 0.13% (±0.05%) were pill marks. We find that our system captures an additional 20% of mentions; these would have been missed by approaches that rely solely on exact string matches. We provide a lexicon of misspellings and synonyms for 2978 drugs and a word embedding model trained on a health-oriented subset of Reddit.
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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.