A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages
This addresses the problem of limited multilingual data for adverse drug reaction detection in healthcare, but it is incremental as it extends existing annotation frameworks to new languages.
The authors tackled the lack of non-English clinical corpora for pharmacovigilance by creating a multilingual dataset of adverse drug reaction texts from German, French, and Japanese sources, resulting in a corpus with annotations for 12 entity types, 4 attribute types, and 13 relation types and strong baseline models for entity and relation extraction.
User-generated data sources have gained significance in uncovering Adverse Drug Reactions (ADRs), with an increasing number of discussions occurring in the digital world. However, the existing clinical corpora predominantly revolve around scientific articles in English. This work presents a multilingual corpus of texts concerning ADRs gathered from diverse sources, including patient fora, social media, and clinical reports in German, French, and Japanese. Our corpus contains annotations covering 12 entity types, four attribute types, and 13 relation types. It contributes to the development of real-world multilingual language models for healthcare. We provide statistics to highlight certain challenges associated with the corpus and conduct preliminary experiments resulting in strong baselines for extracting entities and relations between these entities, both within and across languages.