Discipline of Information & Communication Technology, School of Technology, Environments & Design, University of Tasmania Sandy Bay Campus, Launceston, Australia.
J Med Syst. 2019 May 23;43(7):200. doi: 10.1007/s10916-019-1262-0.
Dialogue-based simulation is a real-world practice technique for medical and clinical education that provides students with an opportunity to train using hands-on experiences without putting actual patients being put at risk. In this paper, a 3D interactive dialogue-based training and assessment system that supports the detailed development of clinical trial competency for medical students in a distributed virtual environment was proposed. For clinical training, MCRDR-based natural language understanding to realize the semantic representation of written dialog from the most relevant inference results was applied, and on the basis of this, a convolutional neural network model was also used to make the generated inference more exact and reliable. For clinical assessment, the dialogue-driven competency method was used to encompass medical knowledge, communication skill as well as professionalism skill based on the collected dialogue information. Finally, the potential of the created system was demonstrated with several clinical cases. The preliminary results indicate that the system demonstrates the potential of providing efficient training and flexible assessment, while saving time, improving practical skills and making students more confident.
基于对话的模拟是一种医学和临床教育的真实实践技术,它为学生提供了在不将实际患者置于风险之中的情况下进行实际操作经验培训的机会。本文提出了一种支持在分布式虚拟环境中为医学生详细发展临床试验能力的 3D 交互式基于对话的培训和评估系统。在临床培训方面,应用基于 MCRDR 的自然语言理解来实现从最相关的推理结果中对书面对话的语义表示,在此基础上,还使用了卷积神经网络模型来使生成的推理更加准确和可靠。在临床评估方面,根据收集到的对话信息,使用对话驱动的能力方法涵盖医学知识、沟通技巧和专业技能。最后,通过几个临床病例展示了所创建系统的潜力。初步结果表明,该系统具有提供高效培训和灵活评估的潜力,同时节省时间、提高实践技能并使学生更有信心。