论文标题
自动医疗咨询系统的基准：框架，任务和数据集
A Benchmark for Automatic Medical Consultation System: Frameworks, Tasks and Datasets
论文作者
论文摘要
近年来，利用机器学习来提高自动医疗咨询的效率并增强患者体验，这引起了人们的兴趣。在本文中，我们提出了两个框架来支持自动医疗咨询，即医生对话理解和面向任务的互动。我们创建了一个新的大型医疗对话数据集，其中包含多级良好的注释，并建立了五个独立任务，包括指定的实体识别，对话法分类，症状标签推理，医疗报告生成和面向诊断的对话政策。我们为每个任务报告了一组基准结果，其中显示了数据集的可用性，并为将来的研究设定了基准。代码和数据均可从https://github.com/lemuria-wchen/imcs21获得。
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 https://github.com/lemuria-wchen/imcs21.
