An Intelligent Virtual Standard Patient for Medical Students Training Based on Oral Knowledge Graph

作     者：Song, Wenfeng Hou, Xia Li, Shuai Chen, Chenglizhao Gao, Danyang Wang, Xian'e Sun, Yuzhe Hou, Jianxia Hao, Aimin 

作者机构：Beijing Informat Sci & Technol Univ Comp Sch Beijing 100101 Peoples R China Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China China Univ Petr East China Coll Comp Sci & Technol Qingdao 266580 Peoples R China Peking Univ Dept Periodontol Sch & Hosp Stomatol Beijing Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON MULTIMEDIA》 (IEEE Trans Multimedia)

年 卷 期：2023年第25卷

页      面：6132-6145页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Beijing Natural Science Foundation National Natural Science Foundation of China R&D Program of Beijing Municipal Education Commission [KM202211232003] Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University [VRLAB2022A02] Research Unit of Virtual Human and Virtual Surgery, Chinese Academy of Medical Sciences [2019RU004] Beijing Advanced Innovation Center for Biomedical Engineering [ZF138G1714] 

主　　题：Intelligent Training Oral Knowledge Graph Virtual Standard Patient 

摘      要：Virtual standard patient (VSP) is in high demand for medical students diagnosis ability training in an efficient manner. Different from the traditional conversation system in medical dialogue generation, VSP needs a novel conversation paradigm to act as the patient instead of the doctor. However, existing conversation techniques still have limited ability in terms of generation of symptoms exhibited by patients with the personalized and knowledge-centered expressions. To alleviate these problems, we propose to construct a novel oral knowledge graph, which sufficiently provides medical clues of the certain disease. Accordingly, the VSP could accurately interact with the dentists for their underlying intention and express the symptoms characters in a natural style. To efficiently retrieve the related disease clues, the symptoms descriptions of the oral diseases are encoded into the oral knowledge graph, which could well organize the disease-centered symptom entities and speaking styles. Moreover, to transfer the common sense knowledge from existing large scale of medical knowledge graph to the specific oral knowledge graph, a coupled pre-trained Bert models is further designed to learn the related medical knowledge from coarse-level to fine-level hierarchically. Finally, a series of well-designed personalized templates are proposed to generate plausible and realistic answers in condition of the certain disease. We also conduct extensive user studies to demonstrate that the VSP satisfies the medical students diagnosis practice requirement in terms of naturalness, realism, and topic relevance.