A Sui Generis QA Approach using RoBERTa for Adverse Drug Event Identification
This work addresses drug-safety monitoring for healthcare researchers, but it is incremental as it builds on existing QA and RoBERTa approaches.
The paper tackled the problem of extracting adverse drug events from biomedical text by introducing a question answering framework using RoBERTa, which achieved a 9.53% improvement in F1-Score over prior methods.
Extraction of adverse drug events from biomedical literature and other textual data is an important component to monitor drug-safety and this has attracted attention of many researchers in healthcare. Existing works are more pivoted around entity-relation extraction using bidirectional long short term memory networks (Bi-LSTM) which does not attain the best feature representations. In this paper, we introduce a question answering framework that exploits the robustness, masking and dynamic attention capabilities of RoBERTa by a technique of domain adaptation and attempt to overcome the aforementioned limitations. Our model outperforms the prior work by 9.53% F1-Score.