论文标题
Lingyi：基于多模式知识图的医学对话问答答案系统
LingYi: Medical Conversational Question Answering System based on Multi-modal Knowledge Graphs
论文作者
论文摘要
医学对话系统可以减轻医生的负担，并提高医疗保健的效率，尤其是在大流行期间。本文介绍了基于多模式知识图的医学对话问题回答（CQA）系统，即“ Lingyi”，该系统被设计为维持高灵活性的管道框架。我们的系统采用自动医疗程序，包括医疗分类，咨询，图像文本药物建议和记录。为了与患者进行知识接地的对话，我们首先构建了中国医学多模式知识图（CM3KG），并收集大型中国医学CQA（CMCQA）数据集。与其他现有的医疗询问系统相比，我们的系统采用了几种最先进的技术，包括医疗实体的歧义和医疗对话生成，这更友好地为患者提供医疗服务。此外，我们已经开源的代码，其中包含后端模型和前端网页，网址为https://github.com/wengsyx/lingyi。在https://github.com/wengsyx/cm3kg和CMCQA上的数据集在https://github.com/wengsyx/cmcqa上也已发布，以进一步促进未来的研究。
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.
