Medical dialogue information extraction is becoming an increasingly significant problem in modern medical care. It is difficult to extract key information from electronic medical records (EMRs) due to their large numbers. Previously, researchers proposed attention-based models for retrieving features from EMRs, but their limitations were reflected in their inability to recognize different categories in medical dialogues. In this paper, we propose a novel model, Expert System and Attention for Labelling (ESAL). We use mixture of experts and pre-trained BERT to retrieve the semantics of different categories, enabling the model to fuse the differences between them. In our experiment, ESAL was applied to a public dataset and the experimental results indicated that ESAL significantly improved the performance of Medical Information Classification.

翻译：医疗对话信息提取正成为现代医疗领域中日益重要的问题。由于电子病历（EMR）数量庞大，从中提取关键信息存在困难。以往研究者提出了基于注意力的模型来从电子病历中获取特征，但这些模型在识别医疗对话中不同类别的能力上存在局限。本文提出了一种新型模型——专家系统与注意力标注（ESAL）。我们利用混合专家系统和预训练的BERT模型来获取不同类别的语义信息，从而使模型能够融合类别间的差异。在我们的实验中，将ESAL应用于公开数据集，实验结果表明ESAL显著提升了医疗信息分类的性能。