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

Chen, Wei ORCID: 0000-0001-9431-9247, Li, Zhiwei, Fang, Hongyi, Yao, Qianyuan, Zhong, Cheng, Hao, Jianye ORCID: 0000-0003-3064-9794, Zhang, Qi, Huang, Xuanjing, Peng, Jiajie ORCID: 0000-0002-3857-7927 and Wei, Zhongyu (2023) A benchmark for automatic medical consultation system: frameworks, tasks and datasets. Bioinformatics (Oxford, England), 39 (1). btac817-.

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Abstract

<h4>Motivation</h4>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.<h4>Results</h4>We report a set of benchmark results for each task, which shows the usability of the dataset and sets a baseline for future studies.<h4>Availability and implementation</h4>Both code and data are available from https://github.com/lemuria-wchen/imcs21.<h4>Supplementary information</h4>Supplementary data are available at Bioinformatics online.

Item Type:	Article
Uncontrolled Keywords:	Humans, Benchmarking, Referral and Consultation, Machine Learning
Depositing User:	Symplectic Admin
Date Deposited:	26 Mar 2024 09:37
Last Modified:	26 Mar 2024 15:35
DOI:	10.1093/bioinformatics/btac817
Open Access URL:	https://doi.org/10.1093/bioinformatics/btac817
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3179913