Opinion observer: analyzing and comparing opinions on the Web
WWW '05 Proceedings of the 14th international conference on World Wide Web
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
A fuzzy logic-based computational recognition-primed decision model
Information Sciences: an International Journal
IEEE Transactions on Information Technology in Biomedicine - Special section on new and emerging technologies in bioinformatics and bioengineering
BioNLP '10 Proceedings of the 2010 Workshop on Biomedical Natural Language Processing
Mining Unexpected Temporal Associations: Applications in Detecting Adverse Drug Reactions
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
SHB 2012: international workshop on smart health and wellbeing
Proceedings of the 21st ACM international conference on Information and knowledge management
ICSH'13 Proceedings of the 2013 international conference on Smart Health
ACM Transactions on Management Information Systems (TMIS) - Special Issue on Informatics for Smart Health and Wellbeing
Text classification for assisting moderators in online health communities
Journal of Biomedical Informatics
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Adverse Drug Reactions (ADRs) represent a serious problem all over the world. They may complicate a patient's medical conditions and increase the morbidity, even mortality. Drug safety currently depends heavily on post-marketing surveillance, because pre-marketing review process cannot identify all possible adverse drug reactions in that it is limited by scale and time span. However, current post-marketing surveillance is conducted through centralized volunteering reporting systems, and the reporting rate is low. Consequently, it is difficult to detect the adverse drug reactions signals in a timely manner. To solve this problem, many researchers have explored methods to detect ADRs in electronic health records. Nevertheless, we only have access to electronic health records form particular health units. Aggregating and integrating electronic health records from multiple sources is rather challenging. With the advance of Web 2.0 technologies and the popularity of social media, many health consumers are discussing and exchanging health-related information with their peers. Many of this online discussion involve adverse drug reactions. In this work, we propose to use association mining and Proportional Reporting Ratios to mine the associations between drugs and adverse reactions from the user contributed content in social media. We have conducted an experiment using ten drugs and five adverse drug reactions. The FDA alerts are used as the gold standard to test the performance of the proposed techniques. The result shows that the metrics leverage, lift, and PRR are all promising to detect the adverse drug reactions reported by FDA. However, PRR outperformed the other two metrics.