MedConQA: Medical Conversational Question Answering System based on Knowledge Graphs
How to use
We offer three experience types:
1. Triage You can take your symptoms as input, and we will triage it.
2. Dagnosis You can ask any medical questions
3. Summary Take the doctor-patient dialogue as input, and the medical record will be output
We offer three input mode:
1.interactive Support human-computer interaction experience demo
2.api Provide API interface for direct call
3.batch Provide input files for batch processing
Example
python main.py --mode api --type Dagnosis --message 我肚子好疼
python main.py --mode interactiv --type Dagnosis
python main.py --mode batch --type Summary --file_name input.csv --result_file_name result.csv
Cite
@inproceedings{xia-etal-2022-medconqa,
title = "{M}ed{C}on{QA}: Medical Conversational Question Answering System based on Knowledge Graphs",
author = "Xia, Fei and
Li, Bin and
Weng, Yixuan and
He, Shizhu and
Liu, Kang and
Sun, Bin and
Li, Shutao and
Zhao, Jun",
booktitle = "Proceedings of the The 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = dec,
year = "2022",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-demos.15",
pages = "148--158",
abstract = "The medical conversational system can relieve doctors{'} burden and improve healthcare efficiency, especially during the COVID-19 pandemic. However, the existing medical dialogue systems have the problems of weak scalability, insufficient knowledge, and poor controllability. Thus, we propose a medical conversational question-answering (CQA) system based on the knowledge graph, namely MedConQA, which is designed as a pipeline framework to maintain high flexibility. Our system utilizes automated medical procedures, including medical triage, consultation, image-text drug recommendation, and record. Each module has been open-sourced as a tool, which can be used alone or in combination, with robust scalability. Besides, to conduct knowledge-grounded dialogues with users, we first construct a Chinese Medical Knowledge Graph (CMKG) and collect a large-scale Chinese Medical CQA (CMCQA) dataset, and we design a series of methods for reasoning more intellectually. Finally, we use several state-of-the-art (SOTA) techniques to keep the final generated response more controllable, which is further assured by hospital and professional evaluations. We have open-sourced related code, datasets, web pages, and tools, hoping to advance future research.",
}