Name
Abstract | ||
|---|---|---|
Adverse drug reactions (ADRs) are one of the leading causes of mortality in health care. Current ADR surveillance systems are often associated with a substantial time lag before such events are officially published. On the other hand, online social media such as Twitter contain information about ADR events in real-time, much before any official reporting. Current state-of-the-art in ADR mention extraction uses Recurrent Neural Networks (RNN), which typically need large labeled corpora. Towards this end, we propose a multi-task learning based method which can utilize a similar auxiliary task (adverse drug event detection) to enhance the performance of the main task, i.e., ADR extraction. Furthermore, in absence of the auxiliary task dataset, we propose a novel joint multi-task learning method to automatically generate weak supervision dataset for the auxiliary task when a large pool of unlabeled tweets is available. Experiments with similar to 0.48M tweets show that the proposed approach outperforms the state-of-the-art methods for the ADR mention extraction task by similar to 7.2 % in terms of F1 score. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-76941-7_5 | ADVANCES IN INFORMATION RETRIEVAL (ECIR 2018) |
Keywords | DocType | Volume |
Multi-task learning,Pharmacovigilance,Neural networks | Conference | 10772 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
18 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| shashank gupta | 1 | 60 | 11.35 |
| Manish Gupta | 2 | 1358 | 98.09 |
| Vasudeva Varma | 3 | 640 | 95.84 |
| Sachin Pawar | 4 | 14 | 8.42 |
| Nitin Ramrakhiyani | 5 | 7 | 5.67 |
| Girish K. Palshikar | 6 | 34 | 6.63 |