Independence and commitment: assumptions for rapid training and execution of rule-based POS taggers
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Assignment Problems
Bioinformatics
Journal of Biomedical Informatics
Predicting thread discourse structure over technical web forums
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Journal of Biomedical Informatics
Proceedings of the Fifth Balkan Conference in Informatics
Social media mining for drug safety signal detection
Proceedings of the 2012 international workshop on Smart health and wellbeing
Data mining methodologies for pharmacovigilance
ACM SIGKDD Explorations Newsletter
Discovering consumer health expressions from consumer-contributed content
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Automatic Drug Adverse Reaction Discovery from Parenting Websites Using Disproportionality Methods
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
ICSH'13 Proceedings of the 2013 international conference on Smart Health
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Adverse reactions to drugs are among the most common causes of death in industrialized nations. Expensive clinical trials are not sufficient to uncover all of the adverse reactions a drug may cause, necessitating systems for post-marketing surveillance, or pharmacovigilance. These systems have typically relied on voluntary reporting by health care professionals. However, self-reported patient data has become an increasingly important resource, with efforts such as MedWatch from the FDA allowing reports directly from the consumer. In this paper, we propose mining the relationships between drugs and adverse reactions as reported by the patients themselves in user comments to health-related websites. We evaluate our system on a manually annotated set of user comments, with promising performance. We also report encouraging correlations between the frequency of adverse drug reactions found by our system in unlabeled data and the frequency of documented adverse drug reactions. We conclude that user comments pose a significant natural language processing challenge, but do contain useful extractable information which merits further exploration.