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
finegrained annotations and establish five independent tasks, 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. Both code and data is available
from this https URL.

本文提出两种支持自动医学咨询的框架，并创建了一个新的大型医学对话数据集，建立了包括命名实体识别，对话行为分类，症状标签推断，医疗报告生成和诊断导向的对话策略在内的五个独立任务。同时，文章提供了每个任务的基准结果，并公开了相关代码和数据集。