Automatically Extracting Information in Medical Dialogue: Expert System And Attention for Labelling
This work addresses the challenge of information extraction in medical care, but it appears incremental as it builds on existing attention-based models with a hybrid approach.
The paper tackled the problem of extracting key information from medical dialogues in electronic medical records by proposing the ESAL model, which improved Medical Information Classification performance on a public dataset.
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.