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

• URL: http://arxiv.org/abs/2204.08997v1
• Date: Tue, 19 Apr 2022 16:43:21 GMT
• Title: A Benchmark for Automatic Medical Consultation System: Frameworks, Tasks and Datasets
• Authors: Wei Chen, Zhiwei Li, Hongyi Fang, Qianyuan Yao, Cheng Zhong, Jianye Hao, Qi Zhang, Xuanjing Huang, J iajie Peng, Zhongyu Wei
• Abstract summary: 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. We report a set of benchmark results for each task, which shows the usability of the dataset and sets a baseline for future studies.
• Score: 70.32630628211803
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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