MedConQA: Medical Conversational Question Answering System based on Knowledge Graphs

Fei Xia, Bin Li, Yixuan Weng, Shizhu He, Kang Liu, Bin Sun, Shutao Li, Jun Zhao


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.
Anthology ID:
2022.emnlp-demos.15
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Month:
December
Year:
2022
Address:
Abu Dhabi, UAE
Editors:
Wanxiang Che, Ekaterina Shutova
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
148–158
Language:
URL:
https://aclanthology.org/2022.emnlp-demos.15
DOI:
10.18653/v1/2022.emnlp-demos.15
Bibkey:
Cite (ACL):
Fei Xia, Bin Li, Yixuan Weng, Shizhu He, Kang Liu, Bin Sun, Shutao Li, and Jun Zhao. 2022. MedConQA: Medical Conversational Question Answering System based on Knowledge Graphs. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 148–158, Abu Dhabi, UAE. Association for Computational Linguistics.
Cite (Informal):
MedConQA: Medical Conversational Question Answering System based on Knowledge Graphs (Xia et al., EMNLP 2022)
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PDF:
https://aclanthology.org/2022.emnlp-demos.15.pdf