Applicability Condition Extraction for Therapeutic Drug-Disease Relations
It addresses the overlooked need for context-specific condition extraction in drug-disease relations, providing a benchmark and method for clinical decision support.
The paper introduces a new task of extracting applicability conditions for therapeutic drug-disease relations from biomedical literature, creating a dataset of 1,119 annotated drug-disease pairs and proposing a LoRA-enhanced method that outperforms strong baselines across evaluation settings.
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