论文标题
自动提取医学对话中的信息：专家系统和标签的关注
Automatically Extracting Information in Medical Dialogue: Expert System And Attention for Labelling
论文作者
论文摘要
医疗对话信息提取在现代医疗保健中越来越重大问题。由于它们的数量很大，很难从电子病历（EMR）中提取关键信息。以前，研究人员提出了基于注意力的基于注意力的模型，以从EMR中检索特征，但是它们的局限性反映在无法识别医疗对话中的不同类别的情况下。在本文中，我们提出了一个新颖的模型，专家系统和标签（ESAL）的关注。我们使用专家和预训练的BERT的混合物来检索不同类别的语义，从而使模型能够融合它们之间的差异。在我们的实验中，ESAL应用于公共数据集，实验结果表明ESAL显着改善了医疗信息分类的性能。
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.
