Applicability Condition Extraction for Therapeutic Drug-Disease Relations
Quick Answer
This paper introduces the task of applicability condition extraction for drug-disease relations, presenting a novel dataset with 1,119 annotated pairs.
Quick Take
A new method enhancing LoRA shows superior performance over existing baselines in extracting context-specific conditions from biomedical literature.
Key Points
- First dataset with 1,119 drug-disease-applicability condition triples created.
- New method enhances LoRA to improve extraction performance.
- Systematic evaluation shows consistent outperformance over strong baselines.
- Focus on context-specific conditions crucial for clinical decision-making.
Paper Resources
Article Excerpt
From source RSS / original summaryarXiv:2606. 14031v1 Announce Type: new Abstract: Identifying conditions that a certain drug takes therapeutic effect on a target disease is crucial for clinical decision-making support. However, most existing biomedical information extraction methods have focused on identifying only relations between drugs and diseases, while largely overlooking the context-specific conditions where such relations can apply.
To address this problem, we introduce the task of applicability condition extraction for therapeutic drug--disease relations from biomedical research literature. We create the first dataset that has manually annotated triples of drugs, diseases, and applicability conditions on biomedical paper abstracts with 1,119 drug-disease pairs. Using this dataset, we systematically evaluate the performance of a range of existing methods.
In addition, we propose a new method that enhances LoRA to consider relations between drugs and diseases. Our method consistently outperforms strong baselines across different evaluation settings. The source code and dataset of this paper can be obtained from: https://github. com/guantingluo98/Drug-ACE
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