Using natural language processing (NLP) technologies to develop medical chatbots makes the diagnosis of the patient more convenient and efficient, which is a typical application in healthcare AI. Because of its importance, lots of research have been come out. Recently, the neural generative models have shown their impressive ability as the core of chatbot, while it cannot scale well when directly applied to medical conversation due to the lack of medical-specific knowledge. To address the limitation, a scalable Medical Knowledge Assisted mechanism, MKA, is proposed in this paper. The mechanism aims to assist general neural generative models to achieve better performance on the medical conversation task. The medical-specific knowledge graph is designed within the mechanism, which contains 6 types of medical-related information, including department, drug, check, symptom, disease, food. Besides, the specific token concatenation policy is defined to effectively inject medical information into the input data. Evaluation of our method is carried out on two typical medical datasets, MedDG and MedDialog-CN. The evaluation results demonstrate that models combined with our mechanism outperform original methods in multiple automatic evaluation metrics. Besides, MKA-Bert-GPT achieves state-of-the-art performance. The open-sourced codes are public: https://github.com/LIANGKE23/Knowledge_Assisted_Medical_Dialogue_Generation_Mechanism

翻译：利用自然语言处理技术开发医疗聊天机器人，可使患者诊断更加便捷高效，这是医疗人工智能领域的典型应用。因其重要性，已有大量研究涌现。近年来，神经生成模型作为聊天机器人的核心展现出卓越能力，但由于缺乏医学领域特定知识，当直接应用于医疗对话时难以扩展。为解决此局限性，本文提出可扩展的医学知识辅助机制MKA。该机制旨在辅助通用神经生成模型在医疗对话任务中取得更优性能。机制内设计了包含科室、药物、检查、症状、疾病、食物六类医疗相关信息的医学领域知识图谱，同时定义了专门的标记拼接策略以有效将医疗信息注入输入数据。我们在两个典型医疗数据集MedDG和MedDialog-CN上进行了方法评估，结果表明，融合该机制的模型在多项目自动评估指标上均优于原始方法。此外，MKA-Bert-GPT实现了最先进性能。开源代码已公开：https://github.com/LIANGKE23/Knowledge_Assisted_Medical_Dialogue_Generation_Mechanism