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

翻译：识别某种药物对目标疾病产生治疗效果的适用条件对临床决策支持至关重要。然而，现有生物医学信息提取方法大多仅聚焦于药物与疾病间的关系识别，而忽略了此类关系适用的特定情境条件。针对这一问题，我们提出从生物医学研究文献中提取药物-疾病治疗关系适用条件的新任务。我们构建了首个在论文摘要上人工标注药物、疾病及适用条件三元组的数据集，包含1,119对药物-疾病关联。基于该数据集，我们系统评估了多种现有方法的性能。此外，我们提出了一种增强LoRA以考量药物与疾病间关系的新方法。该方法在不同评估场景下均持续优于强基线模型。本文源代码及数据集可从以下地址获取：https://github.com/guantingluo98/Drug-ACE