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

2021-11-24 13:11:05
Ilseyar Alimova, Elena Tutubalina
arXiv_CL
             
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.

Abstract (translated)

URL

https://arxiv.org/abs/2111.12477

PDF

https://arxiv.org/pdf/2111.12477.pdf

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