Selection of pseudo-annotated data for adverse drug reaction classification across drug groups
This work addresses the challenge of robust adverse drug reaction monitoring for the biomedical community, but it is incremental as it builds on existing neural models and data strategies.
The study tackled the problem of adverse drug reaction classification across different drug groups by assessing the robustness of state-of-the-art neural architectures and investigating strategies using pseudo-labeled data. The result showed that out-of-dataset experiments revealed performance bottlenecks, while additional pseudo-labeled data improved overall results regardless of the text selection strategy.
Automatic monitoring of adverse drug events (ADEs) or reactions (ADRs) is currently receiving significant attention from the biomedical community. In recent years, user-generated data on social media has become a valuable resource for this task. Neural models have achieved impressive performance on automatic text classification for ADR detection. Yet, training and evaluation of these methods are carried out on user-generated texts about a targeted drug. In this paper, we assess the robustness of state-of-the-art neural architectures across different drug groups. We investigate several strategies to use pseudo-labeled data in addition to a manually annotated train set. Out-of-dataset experiments diagnose the bottleneck of supervised models in terms of breakdown performance, while additional pseudo-labeled data improves overall results regardless of the text selection strategy.