Leveraging Historical Medical Records as a Proxy via Multimodal Modeling and Visualization to Enrich Medical Diagnostic Learning

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...

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Publié dans:IEEE transactions on visualization and computer graphics. - 1996. - 30(2024), 1 vom: 13. Jan., Seite 1238-1248
Auteur principal: Ouyang, Yang (Auteur)
Autres auteurs: Wu, Yuchen, Wang, He, Zhang, Chenyang, Cheng, Furui, Jiang, Chang, Jin, Lixia, Cao, Yuanwu, Li, Quan
Format: Article en ligne
Langue:English
Publié: 2024
Accès à la collection:IEEE transactions on visualization and computer graphics
Sujets:Journal Article Research Support, Non-U.S. Gov't
Description
Résumé: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
Description:Date Completed 28.12.2023
Date Revised 06.01.2025
published: Print-Electronic
Citation Status MEDLINE
ISSN:1941-0506
DOI:10.1109/TVCG.2023.3326929