Summary of A Dataset For Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions Across Languages, by Lisa Raithel et al.
A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages
by Lisa Raithel, Hui-Syuan Yeh, Shuntaro Yada, Cyril Grouin, Thomas Lavergne, Aurélie Névéol, Patrick Paroubek, Philippe Thomas, Tomohiro Nishiyama, Sebastian Möller, Eiji Aramaki, Yuji Matsumoto, Roland Roller, Pierre Zweigenbaum
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed research presents a novel multilingual corpus of texts discussing Adverse Drug Reactions (ADRs), collected from diverse sources including patient forums, social media, and clinical reports in German, French, and Japanese. The corpus is annotated with 12 entity types, four attribute types, and 13 relation types, enabling the development of real-world multilingual language models for healthcare applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a unique dataset that can help identify ADRs by analyzing online discussions. It collects texts from various sources in different languages and labels them to help machines understand what they’re talking about. This is important because it allows for the creation of language models that can work with multiple languages, which is helpful in healthcare. |
