Name
Title | ||
|---|---|---|
SSEL-ADE: A semi-supervised ensemble learning framework for extracting adverse drug events from social media. |
Abstract | ||
|---|---|---|
•We develop a semi-supervised ensemble learning framework for adverse drug event (ADE) relation extraction.•We propose two semi-supervised ensemble algorithms under the guidance of the SSEL-ADE framework.•We develop six concrete semi-supervised ensemble methods under the guidance of the SSEL-ADE framework. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.artmed.2017.10.003 | Artificial Intelligence in Medicine |
Keywords | Field | DocType |
Ensemble learning,Semi-supervised learning,Social media,Adverse drug event extraction | Data mining,Feature vector,Annotation,Social media,Computer science,Pharmacovigilance,Semantic relation,Artificial intelligence,Syntax,Ensemble learning,Machine learning,Relationship extraction | Journal |
Volume | ISSN | Citations |
84 | 0933-3657 | 0 |
PageRank | References | Authors |
0.34 | 39 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing Liu | 1 | 15 | 2.88 |
| Songzheng Zhao | 2 | 12 | 2.67 |
| Gang Wang | 3 | 344 | 97.03 |