A benchmark for automatic medical consultation system: frameworks, tasks and datasets.
Ontology highlight
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.Availability and implementation
Both code and data are available from https://github.com/lemuria-wchen/imcs21.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Chen W
PROVIDER: S-EPMC9848052 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
Publications
A benchmark for automatic medical consultation system: frameworks, tasks and datasets.
Chen Wei W Li Zhiwei Z Fang Hongyi H Yao Qianyuan Q Zhong Cheng C Hao Jianye J Zhang Qi Q Huang Xuanjing X Peng Jiajie J Wei Zhongyu Z
Bioinformatics (Oxford, England) 20230101 1
<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, d ...[more]
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