用迁移学习从医学文本中自动提取药物相互作用关系。
DREaM: Drug-Drug Relation Extraction via Transfer Learning Method
- 先用预训练模型提取药物实体关系，再迁移应用到医学文本
- 大语言模型验证结果显示71条关系一致率高
- 适合医药知识图谱构建与副作用预测研究者
药物间关系抽取对识别药物相互作用和预测副作用至关重要。随着机器学习方法和大型医学文本数据库的发展，相比依赖专家知识的传统方法，该任务的低成本自动化成为可能。然而，目前尚缺乏专门针对药物关系抽取的数据集，因此需借助迁移学习来推进。本文提出DREAM方法：首先利用训练好的关系抽取模型发现实体间关系，再将其应用于医学文本语料库，构建药物关系本体；随后通过大语言模型对提取结果进行验证。定量结果显示，大语言模型与从部分PubMed摘要中提取的71条关系达成一致。定性分析表明，该方法可揭示医学领域中的语义模糊性，凸显该任务固有的挑战。
原文摘要 · Abstract (English)
Relation extraction between drugs plays a crucial role in identifying drug drug interactions and predicting side effects. The advancement of machine learning methods in relation extraction, along with the development of large medical text databases, has enabled the low cost extraction of such relations compared to other approaches that typically require expert knowledge. However, to the best of our knowledge, there are limited datasets specifically designed for drug drug relation extraction currently available. Therefore, employing transfer learning becomes necessary to apply machine learning methods in this domain. In this study, we propose DREAM, a method that first employs a trained relation extraction model to discover relations between entities and then applies this model to a corpus of medical texts to construct an ontology of drug relationships. The extracted relations are subsequently validated using a large language model. Quantitative results indicate that the LLM agreed with 71 of the relations extracted from a subset of PubMed abstracts. Furthermore, our qualitative analysis indicates that this approach can uncover ambiguities in the medical domain, highlighting the challenges inherent in relation extraction in this field.
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