论文标题
Bertchem-DDI：使用化学结构信息改善了文本的药物毒品相互作用预测
BERTChem-DDI : Improved Drug-Drug Interaction Prediction from text using Chemical Structure Information
论文作者
论文摘要
从预训练的语言模型中获得的嵌入的传统生物医学版本最近显示了医疗领域中关系提取（RE）任务的最新结果。在本文中，我们探讨了如何以药物分子结构形式获得的域知识，以预测文本语料库的药物毒用相互作用。我们提出了一种方法，即Bertchem-DDI，以有效地结合从药物的丰富化学结构以及基于现成的域特异性生物Biobert嵌入基于嵌入的重新结构的药物嵌入。在2013年DDIEXTRACTION 2013语料库上进行的实验清楚地表明，该策略将其他强质体体系结构提高了3.4 \％宏F1得分。
Traditional biomedical version of embeddings obtained from pre-trained language models have recently shown state-of-the-art results for relation extraction (RE) tasks in the medical domain. In this paper, we explore how to incorporate domain knowledge, available in the form of molecular structure of drugs, for predicting Drug-Drug Interaction from textual corpus. We propose a method, BERTChem-DDI, to efficiently combine drug embeddings obtained from the rich chemical structure of drugs along with off-the-shelf domain-specific BioBERT embedding-based RE architecture. Experiments conducted on the DDIExtraction 2013 corpus clearly indicate that this strategy improves other strong baselines architectures by 3.4\% macro F1-score.
