ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2014
Pharmacovigilance on twitter? Mining tweets for adverse drug reactions.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
What it found
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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.
The trial behind it
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Who cites it
42 citing papers in PubMed.
- Drug_SNSMiner: standard pharmacovigilance pipeline for detection of adverse drug reaction using SNS data.Scientific reports · 2023Article
- Can accurate demographic information about people who use prescription medications nonmedically be derived from Twitter?Proceedings of the National Academy of Sciences of the United States of America · 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
- Twitter Usage Among Physicians From 2016 to 2020: Algorithm Development and Longitudinal Analysis Study.Journal of medical Internet research · 2022Article
- Explainable detection of adverse drug reaction with imbalanced data distribution.PLoS computational biology · 2022Article
- SEED: Symptom Extraction from English Social Media Posts using Deep Learning and Transfer Learning.medRxiv : the preprint server for health sciences · 2022Article
- Physical Activity, Sedentary Behavior, and Sleep on Twitter: Multicountry and Fully Labeled Public Data Set for Digital Public Health Surveillance Research.JMIR public health and surveillance · 2022Article
- Using weak supervision to generate training datasets from social media data: a proof of concept to identify drug mentions.Neural computing & applications · 2021Article
- Developing an Automatic System for Classifying Chatter About Health Services on Twitter: Case Study for Medicaid.Journal of medical Internet research · 2021Article
- Article
- Public Perspectives on Anti-Diabetic Drugs: Exploratory Analysis of Twitter Posts.JMIR diabetes · 2021Article
- Evaluating Behavioral and Linguistic Changes During Drug Treatment for Depression Using Tweets in Spanish: Pairwise Comparison Study.Journal of medical Internet research · 2020Article
- A scoping review of the use of Twitter for public health research.Computers in biology and medicine · 2020Article
- Social Media Mining Toolkit (SMMT).Genomics & informatics · 2020Article
- Assessment of Beliefs and Attitudes About Statins Posted on Twitter: A Qualitative Study.JAMA network open · 2020Article
- Analyzing Trends of Loneliness Through Large-Scale Analysis of Social Media Postings: Observational Study.JMIR mental health · 2020Article
- Leveraging digital media data for pharmacovigilance.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- Early Detection of Adverse Drug Reactions in Social Health Networks: A Natural Language Processing Pipeline for Signal Detection.JMIR public health and surveillance · 2019Article
- The Adverse Drug Reactions From Patient Reports in Social Media Project: Protocol for an Evaluation Against a Gold Standard.JMIR research protocols · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Recent research has shown that Twitter data analytics can have broad implications on public health research. However, its value for pharmacovigilance has been scantly studied - with health related forums and community support groups preferred for the task. We present a systematic study of tweets collected for 74 drugs to assess their value as sources of potential signals for adverse drug reactions (ADRs). We created an annotated corpus of 10,822 tweets. Each tweet was annotated for the presence or absence of ADR mentions, with the span and Unified Medical Language System (UMLS) concept ID noted for each ADR present. Using Cohen's kappa1, we calculated the inter-annotator agreement (IAA) for the binary annotations to be 0.69. To demonstrate the utility of the corpus, we attempted a lexicon-based approach for concept extraction, with promising success (54.1% precision, 62.1% recall, and 57.8% F-measure). A subset of the corpus is freely available at: http://diego.asu.edu/downloads.
Indexed as
Identifiers
25954400PMC4419871What OpenQuestion holds
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.