Abstract
Rapid growth of online health social networks has enabled patients to communicate more easily with each other. This way of exchange of opinions and experiences has provided a rich source of information about drugs and their effectiveness and more importantly, their possible adverse reactions. We developed a system to automatically extract mentions of Adverse Drug Reactions (ADRs) from user reviews about drugs in social network websites by mining a set of language patterns. The system applied association rule mining on a set of annotated comments to extract the underlying patterns of colloquial expressions about adverse effects. The patterns were tested on a set of unseen comments to evaluate their performance. We reached to precision of 70.01% and recall of 66.32% and F-measure of 67.96%.
Original language | English (US) |
---|---|
Pages (from-to) | 1019-1026 |
Number of pages | 8 |
Journal | AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium |
Volume | 2011 |
State | Published - 2011 |
ASJC Scopus subject areas
- General Medicine