Name
Abstract | ||
|---|---|---|
Twitter is an attractive source of data for public health surveillance, as it is less hindered by the legal and technical obstacles associated with data sources such as electronic health records. We present a preliminary co-occurrence analysis based on 10% of all tweets from 2014 annotated with medical entities as a first approach to extract health-related facts from Twitter. In this work, co-occurrence of annotated medical entities are used to provide population-scale information about common health issues and related entities, which has potential applications in areas such as pharmacovigilance.
|
Year | Venue | Field |
|---|---|---|
2015 | DTMBIO@CIKM | Data mining,Public health surveillance,World Wide Web,Computer science,Pharmacovigilance |
DocType | ISBN | Citations |
Conference | 978-1-4503-3787-8 | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrew MacKinlay | 1 | 140 | 12.12 |
| Antonio Jimeno-Yepes | 2 | 540 | 33.38 |
| Bo Han | 3 | 593 | 29.85 |