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A Benchmark for Automatic Medical Consultation System: Frameworks, Tasks and Datasets

2022-04-19 16:43:21
Wei Chen, Zhiwei Li, Hongyi Fang, Qianyuan Yao, Cheng Zhong, Jianye Hao, Qi Zhang, Xuanjing Huang, J iajie Peng, Zhongyu Wei
arXiv_CL
                 
Abstract

In recent years, interest has arisen in using machine learning to improve the efficiency of automatic medical consultation and enhance patient experience. In this paper, we propose two frameworks to support automatic medical consultation, namely doctor-patient dialogue understanding and task-oriented interaction. A new large medical dialogue dataset with multi-level fine-grained annotations is introduced and five independent tasks are established, including named entity recognition, dialogue act classification, symptom label inference, medical report generation and diagnosis-oriented dialogue policy. We report a set of benchmark results for each task, which shows the usability of the dataset and sets a baseline for future studies.

Abstract (translated)

URL

https://arxiv.org/abs/2204.08997

PDF

https://arxiv.org/pdf/2204.08997.pdf

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