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

• URL: http://arxiv.org/abs/2211.15544v1
• Date: Mon, 28 Nov 2022 16:49:13 GMT
• Title: Automatically Extracting Information in Medical Dialogue: Expert System And Attention for Labelling
• Authors: Xinshi Wang, Daniel Tang
• Abstract summary: Expert System and Attention for Labelling (ESAL) is a novel model for retrieving features from medical records. We use mixture of experts and pre-trained BERT to retrieve the semantics of different categories. In our experiment, ESAL significantly improved the performance of Medical Information Classification.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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