ArticleBMC medical informatics and decision making2023
Construction of a knowledge graph for breast cancer diagnosis based on Chinese electronic medical records: development and usability study.
Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Dynamic Closed-Loop Medical Record Quality Management Using an AI-Driven Multilevel Quality Control System: Development and Implementation Study.JMIR medical informatics · 2026Article
- Interpretable AI-assisted diagnosis of papillary thyroid cancer cytopathology using graph neural networks and knowledge graphs.Scientific reports · 2025Article
- Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends.MedComm · 2025Review
- Knowledge Graph for Breast Cancer Prevention and Treatment: Literature-Based Data Analysis Study.JMIR medical informatics · 2024Article
- A Visualization Method of Knowledge Graphs for the Computation and Comprehension of Ultrasound Reports.Biomimetics (Basel, Switzerland) · 2023Article
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8 authors.
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Abstract
backgroundElectronic medical records (EMRs) contain a wealth of information related to breast cancer diagnosis and treatment. Extracting relevant features from these medical records and constructing a knowledge graph can significantly contribute to an efficient data analysis and decision support system for breast cancer diagnosis.
methodsAn approach was proposed to develop a workflow for effectively extracting breast cancer-related features from Chinese breast cancer mammography reports and constructing a knowledge graph for breast cancer diagnosis. Firstly, the concept layer of the knowledge graph for breast cancer diagnosis was constructed based on breast cancer diagnosis and treatment guidelines, along with insights from clinical experts. .Next, a BiLSTM-Highway-CRF model was designed to extract the mammography features, which formed the data layer of the knowledge graph. Finally, the knowledge graph was constructed by combining the concept layer and the data layer in a Neo4j graph data platform, and then applied in visualization analysis, semantic query and computer assisted diagnosis.
resultsMammographic features were extracted from a total of 1171 mammography examination reports. The overall extraction performance of the model achieved an accuracy rate of 97.16%, a recall rate of 98.06%, and a F1 score of 97.61%. Additionally, 47,660 relationships between entities were identified based on the four different types of relationships defined in the concept layer. The knowledge graph for breast cancer diagnosis was constructed after inputting mammographic features and relationships into the Neo4j graph data platform. The model was assessed from the concept layer, data layer, and application layer perspectives, and showed promising results.
conclusionsThe proposed workflow is applicable for constructing knowledge graphs for breast cancer diagnosis based on Chinese EMRs. This study serves as a reference for the rapid design, construction, and application of knowledge graphs for diagnosis and treatment of other diseases. Furthermore, it offers a potential solution to address the issues of limited data sharing and format inconsistencies present in Chinese EMR data.
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