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...

Description complète

Détails bibliographiques
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
LEADER 01000caa a22002652c 4500
001 NLM363669779
003 DE-627
005 20250305091602.0
007 cr uuu---uuuuu
008 231226s2024 xx |||||o 00| ||eng c
024 7 |a 10.1109/TVCG.2023.3326929  |2 doi 
028 5 2 |a pubmed25n1211.xml 
035 |a (DE-627)NLM363669779 
035 |a (NLM)37874707 
040 |a DE-627  |b ger  |c DE-627  |e rakwb 
041 |a eng 
100 1 |a Ouyang, Yang  |e verfasserin  |4 aut 
245 1 0 |a Leveraging Historical Medical Records as a Proxy via Multimodal Modeling and Visualization to Enrich Medical Diagnostic Learning 
264 1 |c 2024 
336 |a Text  |b txt  |2 rdacontent 
337 |a ƒaComputermedien  |b c  |2 rdamedia 
338 |a ƒa Online-Ressource  |b cr  |2 rdacarrier 
500 |a Date Completed 28.12.2023 
500 |a Date Revised 06.01.2025 
500 |a published: Print-Electronic 
500 |a Citation Status MEDLINE 
520 |a 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 
650 4 |a Journal Article 
650 4 |a Research Support, Non-U.S. Gov't 
700 1 |a Wu, Yuchen  |e verfasserin  |4 aut 
700 1 |a Wang, He  |e verfasserin  |4 aut 
700 1 |a Zhang, Chenyang  |e verfasserin  |4 aut 
700 1 |a Cheng, Furui  |e verfasserin  |4 aut 
700 1 |a Jiang, Chang  |e verfasserin  |4 aut 
700 1 |a Jin, Lixia  |e verfasserin  |4 aut 
700 1 |a Cao, Yuanwu  |e verfasserin  |4 aut 
700 1 |a Li, Quan  |e verfasserin  |4 aut 
773 0 8 |i Enthalten in  |t IEEE transactions on visualization and computer graphics  |d 1996  |g 30(2024), 1 vom: 13. Jan., Seite 1238-1248  |w (DE-627)NLM098269445  |x 1941-0506  |7 nnas 
773 1 8 |g volume:30  |g year:2024  |g number:1  |g day:13  |g month:01  |g pages:1238-1248 
856 4 0 |u http://dx.doi.org/10.1109/TVCG.2023.3326929  |3 Volltext 
912 |a GBV_USEFLAG_A 
912 |a SYSFLAG_A 
912 |a GBV_NLM 
912 |a GBV_ILN_350 
951 |a AR 
952 |d 30  |j 2024  |e 1  |b 13  |c 01  |h 1238-1248