Name
Title | ||
|---|---|---|
Towards internet-age pharmacovigilance: extracting adverse drug reactions from user posts to health-related social networks |
Abstract | ||
|---|---|---|
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. |
Year | Venue | Keywords |
|---|---|---|
2010 | BioNLP '10 Proceedings of the 2010 Workshop on Biomedical Natural Language Processing | health care professional,user comment,adverse reaction,adverse drug reaction,towards internet-age pharmacovigilance,necessitating system,common cause,health-related social network,expensive clinical trial,unlabeled data,user post,self-reported patient data,encouraging correlation |
Field | DocType | Citations |
Data mining,Internet privacy,Social network,MedWatch,Clinical trial,Artificial intelligence,Natural language processing,Developed country,Pharmacovigilance,Drug,Medicine,Information Age,Health care | Conference | 88 |
PageRank | References | Authors |
4.63 | 4 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Robert Leaman | 1 | 914 | 39.98 |
| Laura Wojtulewicz | 2 | 90 | 5.39 |
| Ryan Sullivan | 3 | 107 | 5.71 |
| Annie Skariah | 4 | 88 | 4.63 |
| Jian Yang | 5 | 88 | 4.63 |
| Graciela Gonzalez | 6 | 624 | 39.60 |