Leveraging Historical Medical Records as a Proxy via Multimodal Modeling and Visualization to Enrich Medical Diagnostic Learning
2023
会议录名称IEEE VISUALIZATION CONFERENCE
发表状态已发表
摘要

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.

关键词Multimodal Medical Dataset Visual Analytics Explainable Machine Learning
收录类别SCI
语种英语
文献类型会议论文
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/331127
专题信息科学与技术学院_博士生
信息科学与技术学院_硕士生
信息科学与技术学院_本科生
信息科学与技术学院_PI研究组_李权组
通讯作者Li,Quan
作者单位
1.ShanghaiTech University
2.University of Illinois at Urbana-Champaign
3.ETH Zürich
4.Zhongshan Hospital Fudan University
第一作者单位上海科技大学
通讯作者单位上海科技大学
第一作者的第一单位上海科技大学
推荐引用方式
GB/T 7714
Ouyang,Yang,Wu,Yuchen,Wang,He,et al. Leveraging Historical Medical Records as a Proxy via Multimodal Modeling and Visualization to Enrich Medical Diagnostic Learning[C],2023.
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