A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining (P19-1)
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| Challenge: | Existing methods of identifying ADRs are reliable but time-consuming and offer a limited amount of ADR relevant information. |
| Approach: | They propose a neural network-inspired multi-task learning framework that can simultaneously extract ADRs from various sources. |
| Outcome: | The proposed framework achieves state-of-the-art performance on three publicly available real-world benchmark pharmacovigilance datasets, a Twitter dataset from PSB 2016 Social Me- dia Shared Task, CADEC corpus and Medline ADR corpus. |
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