Research on the construction and application of pathology knowledge graph

-д хадгалсан:

Номзүйн дэлгэрэнгүй
-д хэвлэсэн:	BMC Medical Education vol. 25 (2025), p. 1-12
Үндсэн зохиолч:	Hong, Wei
Бусад зохиолчид:	Liu, Xue, Cao, Huiling, Qin, Weiqi, Ma, Qun, Kong, Lingling
Хэвлэсэн:	Springer Nature B.V.
Нөхцлүүд:	Teaching Medical education Coding theory Questionnaires Educational technology Cognition & reasoning Efficiency Etiology Digital transformation Flipped classroom Knowledge Quasi-experimental methods Learning Semantics Design of experiments Clinical medicine Ontology Medical students Pathology Interviews Use statistics Data collection Morphology Instructional scaffolding Instructional Improvement Atlases Knowledge Representation Medical Evaluation Instructional Effectiveness Course Content Learner Engagement Constructivism (Learning) Methods Research Educational Strategies Algorithms Educational Resources Control Groups Influence of Technology Learning Theories Intelligence Data Analysis
Онлайн хандалт:	Citation/Abstract Full Text Full Text - PDF
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Тодорхойлолт
Хураангуй:	BackgroundDigital transformation in pathology education faces three bottlenecks: fragmented knowledge transfer, low morphological diagnostic accuracy, and weak clinical reasoning. While knowledge graphs (KGs) offer potential solutions, existing medical KG lack multimodal integration and competency assessment. We designed an integrated Multimodal Knowledge Graph (MKG) with O-PIRTAS pedagogy to bridge these gaps.MethodsFollowing Design Science Research Methodology, we built a pathology-specific MKG featuring: (1) Semantic modeling of disease mechanisms (etiology-pathogenesis-morphology-clinical), (2) Cross-modal alignment of digital slides/animations/clinical cases, (3) Embedded metrics (KII/MDA/CCAE) for competency quantification. A quasi-experiment with 533 medical students (2022 cohort control: n = 275; 2023 MKG-O-PIRTAS: n = 258) evaluated outcomes via exam scores, validated questionnaires, and stratified interviews.ResultsThe MKG-O-PIRTAS group achieved significantly higher adjusted exam scores (76.14 vs. 73.72, p = 0.033) and 86% lower misdiagnosis rate in high performers (p = 0.015). Cognitive load diverged markedly (57.5 vs. 75.5, p = 0.007), with high performers dynamically contextualizing MKG nodes into clinical reasoning, while novices required scaffolded pathways. Over 80% of students endorsed enhanced knowledge integration and process optimization.ConclusionThe MKG-O-PIRTAS artifact transforms scattered pathology knowledge into actionable clinical reasoning scaffolds, proving effective for personalized competency development. Future work will scale adaptive scaffolding and integrate real-time EMR modules, establishing a replicable paradigm for medical education intelligence.
ISSN:	1472-6920
DOI:	10.1186/s12909-025-07566-0
Эх сурвалж:	Healthcare Administration Database