TwiMed: Twitter and PubMed Comparable Corpus of Drugs, Diseases, Symptoms, and Their Relations.

Nestor Alvaro Yusuke Miyao Nigel Collier

Published in: JMIR public health and surveillance (2017)

We 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.

Keyphrases

adverse drug
social media
drug induced
electronic health record
big data
emergency department
machine learning
artificial intelligence
data analysis
single cell
sleep quality