In this repository, we share a medical dialogue annotation tool. It is also the web-based tool that mentioned in the paper A Benchmark for Automatic Medical Consultation System: Frameworks, Tasks and Datasets. The tool is based on Django that supports multi-level (token-level, utterance-level and dialog-level) annotation for medical dialogue.
Specifically, at the token-level, it supports to identify medical named entities by BIO scheme and standardize them; at the utterance-level, it supports to classify each utterance (doctor's or patient's), which in our case refers to the dialog intents; at the dialog-level, it supports to collect medical reports, which are required to conform to a specific format and are manually written by annotators.
The project is jointly developed by Qianyuan Yao and Hongyi Fang from Fudan DISC.
- python==3.8
- django==3.0
- pandas
The project uses sqlite database by default , you can change database settings in medisite/settings.py.
Create a Django administrator account to access the database on http://127.0.0.1:8000/admin/.
python manage.py createsuperuserRun the annotation tool and log in http://127.0.0.1:8000 with the test account.
- Username: test
- Password: test
We provide a service website for testing, which host on http://210.16.187.147:60028, and login the administrator website on http://210.16.187.147:60028/admin.
python manage.py runserverThe project also provides a complete PDF document here as instructions.
- Entity
- Entity Normalization
- Dialog Act
- Medical Report
If you extend or use this work, please cite the paper where it was introduced.
@article{chen2022benchmark,
title={A Benchmark for Automatic Medical Consultation System: Frameworks, Tasks and Datasets},
author={Chen, Wei and Li, Zhiwei and Fang, Hongyi and Yao, Qianyuan and Zhong, Cheng and Hao, Jianye and Zhang, Qi and Huang, Xuanjing and Wei, Zhongyu and others},
journal={arXiv preprint arXiv:2204.08997},
year={2022}
}