A benchmark for automatic medical consultation system: frameworks, tasks and datasets.
other · Level V
Where this comes from
- Record sourced from PubMed, PMID 36539203.
- Also identified by DOI 10.1093/bioinformatics/btac817 and PMC identifier 9848052.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
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. 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 are available from https://github.com/lemuria-wchen/imcs21. Supplementary data are available at Bioinformatics online.
Medical subject headings
- Benchmarking
- Machine Learning