Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's Perspective
This work addresses the challenge of ADR detection in non-English languages for healthcare and patient safety, though it is incremental as it extends existing methods to a new dataset.
The authors tackled the problem of detecting adverse drug reactions (ADRs) in German patient-generated content by creating the first German ADR corpus from a patient forum and using cross-lingual methods, achieving an F1-score of 37.52 for the positive class with fine-tuned XLM-RoBERTa.
In this work, we present the first corpus for German Adverse Drug Reaction (ADR) detection in patient-generated content. The data consists of 4,169 binary annotated documents from a German patient forum, where users talk about health issues and get advice from medical doctors. As is common in social media data in this domain, the class labels of the corpus are very imbalanced. This and a high topic imbalance make it a very challenging dataset, since often, the same symptom can have several causes and is not always related to a medication intake. We aim to encourage further multi-lingual efforts in the domain of ADR detection and provide preliminary experiments for binary classification using different methods of zero- and few-shot learning based on a multi-lingual model. When fine-tuning XLM-RoBERTa first on English patient forum data and then on the new German data, we achieve an F1-score of 37.52 for the positive class. We make the dataset and models publicly available for the community.