Simulation-based Medical Education (SBME) has been developed as a cost-effective means of enhancing the diagnostic skills of novice physicians and interns, thereby mitigating the need for resource-intensive mentor-apprentice training. However, feedback provided in most SBME is often directed towards improving the operational proficiency of learners, rather than providing summative medical diagnoses that result from experience and time. Additionally, the multimodal nature of medical data during diagnosis poses significant challenges for interns and novice physicians, including the tendency to overlook or over-rely on data from certain modalities, and difficulties in comprehending potential associations between modalities. To address these challenges, we present DiagnosisAssistant, a visual analytics system that leverages historical medical records as a proxy for multimodal modeling and visualization to enhance the learning experience of interns and novice physicians. The system employs elaborately designed visualizations to explore different modality data, offer diagnostic interpretive hints based on the constructed model, and enable comparative analyses of specific patients. Our approach is validated through two case studies and expert interviews, demonstrating its effectiveness in enhancing medical training.

翻译：基于模拟的医学教育（SBME）已被开发为一种成本效益高的手段，用于提升新手医生和实习生的诊断技能，从而减少对资源密集型师徒培训的需求。然而，大多数SBME中提供的反馈通常侧重于提高学习者的操作熟练度，而非提供源自经验与时间的总结性医学诊断。此外，诊断过程中医学数据的多模态特性给实习医生和新手医师带来了重大挑战，包括倾向于忽略或过度依赖某些模态的数据，以及难以理解模态之间的潜在关联。为解决这些问题，我们提出了DiagnosisAssistant，一种视觉分析系统，该系统利用历史病历作为多模态建模与可视化的代理，以增强实习医生和新手医师的学习体验。该系统采用精心设计的可视化方法探索不同模态的数据，基于构建的模型提供诊断解释性提示，并支持对特定患者的比较分析。通过两项案例研究和专家访谈验证了我们的方法，证明了其在增强医学培训方面的有效性。