灵医：基于多模态知识图谱的医疗对话问答系统
LingYi: Medical Conversational Question Answering System based on Multi-modal Knowledge Graphs
AI总结：
灵医系统基于多模态知识图谱，采用流水线框架实现医疗分诊、咨询、图文药物推荐与记录，利用实体消歧和对话生成技术，并开源代码与数据集以提升医疗对话效率。
摘要：
医疗对话系统可以减轻医生负担并提高医疗保健效率，尤其是在疫情期间。本文提出了一种基于多模态知识图谱的医疗对话问答（CQA）系统，名为“灵医”（LingYi），该系统被设计为流水线框架以保持高灵活性。我们的系统利用自动化医疗流程，包括医疗分诊、咨询、图文药物推荐和记录。为了与患者进行基于知识的对话，我们首先构建了一个中文医疗多模态知识图谱（CM3KG），并收集了一个大规模的中文医疗对话问答（CMCQA）数据集。与现有的其他医疗问答系统相比，我们的系统采用了多项最先进的技术，包括医疗实体消歧和医疗对话生成，从而为患者提供更友好的医疗服务。此外，我们已开源了包含后端模型和前端网页的代码，网址为 https://github.com/WENGSYX/LingYi。同时，我们还发布了数据集，包括 CM3KG（https://github.com/WENGSYX/CM3KG）和 CMCQA（https://github.com/WENGSYX/CMCQA），以进一步促进未来研究。
英文摘要：
The medical conversational system can relieve the burden of doctors and improve the efficiency of healthcare, especially during the pandemic. This paper presents a medical conversational question answering (CQA) system based on the multi-modal knowledge graph, namely "LingYi", 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. To conduct knowledge-grounded dialogues with patients, we first construct a Chinese Medical Multi-Modal Knowledge Graph (CM3KG) and collect a large-scale Chinese Medical CQA (CMCQA) dataset. Compared with the other existing medical question-answering systems, our system adopts several state-of-the-art technologies including medical entity disambiguation and medical dialogue generation, which is more friendly to provide medical services to patients. In addition, we have open-sourced our codes which contain back-end models and front-end web pages at https://github.com/WENGSYX/LingYi. The datasets including CM3KG at https://github.com/WENGSYX/CM3KG and CMCQA at https://github.com/WENGSYX/CMCQA are also released to further promote future research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
