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Medical dialogue information extraction is becoming an increasinglysignificant problem in modern medical care. It is difficult to extract keyinformation from electronic medical records (EMRs) due to their large numbers.Previously, researchers proposed attention-based models for retrieving featuresfrom EMRs, but their limitations were reflected in their inability to recognizedifferent categories in medical dialogues. In this paper, we propose a novelmodel, Expert System and Attention for Labelling (ESAL). We use mixture ofexperts 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 indicatedthat ESAL significantly improved the performance of Medical InformationClassification.

Automatically Extracting Information in Medical Dialogue: Expert System And Attention for Labelling