# Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's Perspective

arXiv: https://arxiv.org/abs/2208.02031 · HTML: https://mlsanity.com/paper/2208.02031/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 47 references that are in the map.
- First posted on arXiv 3 Aug 2022.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
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- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [True Few-Shot Learning with Language Models](https://mlsanity.com/paper/2105.11447/) (2021)
- [CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From Characters](https://mlsanity.com/paper/2010.10392/) (2020)
- [Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion](https://mlsanity.com/paper/1804.07745/) (2018)
- [COMETA: A Corpus for Medical Entity Linking in the Social Media](https://mlsanity.com/paper/2010.03295/) (2020)
- [Towards Realistic Practices In Low-Resource Natural Language Processing: The Development Set](https://mlsanity.com/paper/1909.01522/) (2019)
- [The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews](https://mlsanity.com/paper/2004.03659/) (2020)
- [Exploring a Unified Sequence-To-Sequence Transformer for Medical Product Safety Monitoring in Social Media](https://mlsanity.com/paper/2109.05815/) (2021)
- [From Witch's Shot to Music Making Bones -- Resources for Medical Laymen to Technical Language and Vice Versa](https://mlsanity.com/paper/2005.11494/) (2020)
- [NADE: A Benchmark for Robust Adverse Drug Events Extraction in Face of Negations](https://mlsanity.com/paper/2109.10080/) (2021)

## Built on by (most cited first, 1 of 1)

- [MultiADE: A Multi-domain Benchmark for Adverse Drug Event Extraction](https://mlsanity.com/paper/2405.18015/) (2024)

## Abstract (arXiv)

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.