A Benchmark for Automatic Medical Consultation System: Frameworks, Tasks and Datasets
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.