提取药物治疗特定疾病的适用条件，提升临床决策支持精度
Applicability Condition Extraction for Therapeutic Drug-Disease Relations
- 提出新任务：从文献中抽取药物-疾病治疗关系的适用条件
- 构建首个标注数据集，含1119组药物-疾病-条件三元组
- 改进LoRA模型，显著提升条件抽取准确率，适合医疗AI研究者
确定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，现有生物医学信息抽取方法多仅关注药物与疾病的关系识别，忽视了这些关系适用的具体情境。为此，我们引入治疗性药物-疾病关系适用条件抽取的新任务，并构建首个在生物医学论文摘要上人工标注的三元组数据集，包含1,119组药物-疾病-适用条件组合。基于该数据集，系统评估多种现有方法性能。此外，我们提出一种新方法，通过增强LoRA以捕捉药物与疾病间的关系。该方法在不同评估设置下均优于多个强基线模型。
原文摘要 · Abstract (English)
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.
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