| Literature DB >> 22195162 |
Azadeh Nikfarjam1, Graciela H Gonzalez.
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%.Entities:
Mesh:
Year: 2011 PMID: 22195162 PMCID: PMC3243273
Source DB: PubMed Journal: AMIA Annu Symp Proc ISSN: 1559-4076