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

arXiv: https://arxiv.org/abs/2211.15544 · HTML: https://mlsanity.com/paper/2211.15544/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 28 Nov 2022.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Learning Factored Representations in a Deep Mixture of Experts](https://mlsanity.com/paper/1312.4314/) (2013)
- [Bidirectional Attention Flow for Machine Comprehension](https://mlsanity.com/paper/1611.01603/) (2016)
- [MDFEND: Multi-domain Fake News Detection](https://mlsanity.com/paper/2201.00987/) (2022)
- [Extracting Symptoms and their Status from Clinical Conversations](https://mlsanity.com/paper/1906.02239/) (2019)

## Built on by (most cited first, 2 of 2)

- [GE-Blender: Graph-Based Knowledge Enhancement for Blender](https://mlsanity.com/paper/2301.12850/) (2023)
- [Emotion Detection in Unfix-length-Context Conversation](https://mlsanity.com/paper/2302.06029/) (2023)

## Abstract (arXiv)

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.