ArticleJMIR public health and surveillance2017
TwiMed: Twitter and PubMed Comparable Corpus of Drugs, Diseases, Symptoms, and Their Relations.
Article in JMIR public health and surveillance, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A Review on the Trends in Event Detection by Analyzing Social Media Platforms' Data.Sensors (Basel, Switzerland) · 2022Pooled it
- A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data.International journal of medical informatics · 2019Pooled it
- Detecting Adverse Drug Events in Social Media: A Brief Literature Review.SN computer science · 2026Article
- A Fusion Deep Learning Model for Predicting Adverse Drug Reactions Based on Multiple Drug Characteristics.Life (Basel, Switzerland) · 2025Article
- BioLORD-2023: semantic textual representations fusing large language models and clinical knowledge graph insights.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Transformers and large language models in healthcare: A review.Artificial intelligence in medicine · 2024Review
- Supervised Relation Extraction Between Suicide-Related Entities and Drugs: Development and Usability Study of an Annotated PubMed Corpus.Journal of medical Internet research · 2023Article
- Automatic Extraction of Medication Mentions from Tweets-Overview of the BioCreative VII Shared Task 3 Competition.Database : the journal of biological databases and curation · 2023Article
- A survey on clinical natural language processing in the United Kingdom from 2007 to 2022.NPJ digital medicine · 2022Review
- JCBIE: a joint continual learning neural network for biomedical information extraction.BMC bioinformatics · 2022Article
- Increasing adverse drug events extraction robustness on social media: Case study on negation and speculation.Experimental biology and medicine (Maywood, N.J.) · 2022Article
- Language-agnostic pharmacovigilant text mining to elicit side effects from clinical notes and hospital medication records.Basic & clinical pharmacology & toxicology · 2022Article
- Task reformulation and data-centric approach for Twitter medication name extraction.Database : the journal of biological databases and curation · 2022Article
- SEED: Symptom Extraction from English Social Media Posts using Deep Learning and Transfer Learning.medRxiv : the preprint server for health sciences · 2022Article
- Crowdsourcing and machine learning approaches for extracting entities indicating potential foodborne outbreaks from social media.Scientific reports · 2021Article
- Spontaneously Generated Online Patient Experience of Modafinil: A Qualitative and NLP Analysis.Frontiers in digital health · 2021Article
- Pharmacovigilance with Transformers: A Framework to Detect Adverse Drug Reactions Using BERT Fine-Tuned with FARM.Computational and mathematical methods in medicine · 2021Article
- Prospective Evaluation of Adverse Event Recognition Systems in Twitter: Results from the Web-RADR Project.Drug safety · 2020Article
- Mining Social Media Data for Biomedical Signals and Health-Related Behavior.Annual review of biomedical data science · 2020Article
- Adverse Events in Twitter-Development of a Benchmark Reference Dataset: Results from IMI WEB-RADR.Drug safety · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWork on pharmacovigilance systems using texts from PubMed and Twitter typically target at different elements and use different annotation guidelines resulting in a scenario where there is no comparable set of documents from both Twitter and PubMed annotated in the same manner.
objectiveThis study aimed to provide a comparable corpus of texts from PubMed and Twitter that can be used to study drug reports from these two sources of information, allowing researchers in the area of pharmacovigilance using natural language processing (NLP) to perform experiments to better understand the similarities and differences between drug reports in Twitter and PubMed.
methodsWe produced a corpus comprising 1000 tweets and 1000 PubMed sentences selected using the same strategy and annotated at entity level by the same experts (pharmacists) using the same set of guidelines.
resultsThe resulting corpus, annotated by two pharmacists, comprises semantically correct annotations for a set of drugs, diseases, and symptoms. This corpus contains the annotations for 3144 entities, 2749 relations, and 5003 attributes.
conclusionsWe present a corpus that is unique in its characteristics as this is the first corpus for pharmacovigilance curated from Twitter messages and PubMed sentences using the same data selection and annotation strategies. We believe this corpus will be of particular interest for researchers willing to compare results from pharmacovigilance systems (eg, classifiers and named entity recognition systems) when using data from Twitter and from PubMed. We hope that given the comprehensive set of drug names and the annotated entities and relations, this corpus becomes a standard resource to compare results from different pharmacovigilance studies in the area of NLP.
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What 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.