A benchmark for automatic medical consultation system: frameworks, tasks and datasets

Wei Chen, Zhiwei Li, Hongyi Fang, Qianyuan Yao, Cheng Zhong, Jianye Hao, Qi Zhang, Xuanjing Huang, Jiajie Peng, Zhongyu Wei

Research output: Contribution to journalArticlepeer-review

35 Scopus citations

Abstract

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

Original languageEnglish
Article numberbtac817
JournalBioinformatics
Volume39
Issue number1
DOIs
StatePublished - 1 Jan 2023

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