Selection of pseudo-annotated data for adverse drug reaction classification across drug groups

• URL: http://arxiv.org/abs/2111.12477v1
• Date: Wed, 24 Nov 2021 13:11:05 GMT
• Title: Selection of pseudo-annotated data for adverse drug reaction classification across drug groups
• Authors: Ilseyar Alimova and Elena Tutubalina
• Abstract summary: 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.
• Score: 12.259552039796027
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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