ADRTrace: detecting expected and unexpected adverse drug reactions from user reviews on social media sites

Authors:
Andrew Yates;Nazli Goharian
Affiliations:
Information Retrieval Laboratory, Department of Computer Science, Georgetown University;Information Retrieval Laboratory, Department of Computer Science, Georgetown University
Venue:
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Year:
2013

Citing 2
Cited 2

Espresso: leveraging generic patterns for automatically harvesting semantic relations

ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Towards internet-age pharmacovigilance: extracting adverse drug reactions from user posts to health-related social networks

BioNLP '10 Proceedings of the 2010 Workshop on Biomedical Natural Language Processing

Graded relevance ranking for synonym discovery

Proceedings of the 22nd international conference on World Wide Web companion
A framework for detecting public health trends with Twitter

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Quantified Score

Hi-index	0.00

Visualization

Abstract

We automatically extract adverse drug reactions (ADRs) from consumer reviews provided on various drug social media sites to identify adverse reactions not reported by the United States Food and Drug Administration (FDA) but touted by consumers. We utilize various lexicons, identify patterns, and generate a synonym set that includes variations of medical terms. We identify "expected" and "unexpected" ADRs. Background (drug) language is utilized to evaluate the strength of the detected unexpected ADRs. Evaluation results for our synonym set and ADR extraction are promising.