Towards Internet-Age Pharmacovigilance: Extracting Adverse Drug Reactions from User Posts in Health-Related Social Networks
By Robert Leaman, Laura Wojtulewicz, Ryan Sullivan, Annie Skariah, Jian Yang, Graciela Gonzalez. BioNLP@ACL, 2010
314 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/f26ba61a3b7847be5632e89533f454fc710ebdaf
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Chih-Hsuan Wei
- Zhiyong Lu
- Yifan Peng
- Robert Leaman
- Abeed Sarker
- Jiao Li
- Allan Peter Davis
- Graciela Gonzalez
Citations in the corpus
- Extraction of potential adverse drug events from medical case reports (2012, background): “[4] proposed a lenient NLP model for extracting adverse effects of drugs from social media such as blogs.”
- Utilizing Social Media Data for Pharmacovigilance: A Review (2015, methodology): “Most research tasks have been designed such that evaluations could be performed using existing metrics such as Recall , Precision , F-score and Accuracy [17,34,42,43,45,46,50,57,58,38,39,41,62,65].”
- Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task (2016, methodology): “More recently, a similar set of computational methods has been applied to a number of diverse datasets such as the FDA’s Adverse Event Reporting System (FAERS) (12), electronic medical records (13), tweets, and user comments in social media (14).”
- ADRTrace: Detecting Expected and Unexpected Adverse Drug Reactions from User Reviews on Social Media Sites (2013, background): “Unlike our effort, the authors in [4] mined ADRs from drug discussion forums using a sliding window, suffering from inexact matching.”
- Portable Automatic Text Classification for Adverse Drug Reaction Detection via Multi-corpus Training (2014, background): “[8], who showed that user comments from health-related social networks can reflect known ADRs and potentially generate early warnings about unknown ADRs.”
- Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports (2012, background): “This includes the electronic patient health records, hospital discharge summaries, medical case reports, full text research articles, blogs [4], and news reports [5].”