ArticleJMIR medical informatics2020
Construction of a Digestive System Tumor Knowledge Graph Based on Chinese Electronic Medical Records: Development and Usability Study.
Article in JMIR medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Liver cancer knowledge graph construction based on dynamic entity replacement and masking strategies RoBERTa-wwm-large-BiLSTM-CRF model with clinical Chinese EMRs.Frontiers in artificial intelligence · 2025Article
- MKG-GC: A multi-task learning-based knowledge graph construction framework with personalized application to gastric cancer.Computational and structural biotechnology journal · 2024Article
- Research on Traditional Chinese Medicine: Domain Knowledge Graph Completion and Quality Evaluation.JMIR medical informatics · 2024Article
- BCSLinker: automatic method for constructing a knowledge graph of venous thromboembolism based on joint learning.Frontiers in medicine · 2024Article
- Effect of Artificial Intelligence-based Health Education Accurately Linking System (AI-HEALS) for Type 2 diabetes self-management: protocol for a mixed-methods study.BMC public health · 2023Article
- Automatic knowledge extraction from Chinese electronic medical records and rheumatoid arthritis knowledge graph construction.Quantitative imaging in medicine and surgery · 2023Article
- Heterogeneous graph construction and HinSAGE learning from electronic medical records.Scientific reports · 2022Article
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3 authors.
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Abstract
backgroundWith the increasing incidences and mortality of digestive system tumor diseases in China, ways to use clinical experience data in Chinese electronic medical records (CEMRs) to determine potentially effective relationships between diagnosis and treatment have become a priority. As an important part of artificial intelligence, a knowledge graph is a powerful tool for information processing and knowledge organization that provides an ideal means to solve this problem.
objectiveThis study aimed to construct a semantic-driven digestive system tumor knowledge graph (DSTKG) to represent the knowledge in CEMRs with fine granularity and semantics.
methodsThis paper focuses on the knowledge graph schema and semantic relationships that were the main challenges for constructing a Chinese tumor knowledge graph. The DSTKG was developed through a multistep procedure. As an initial step, a complete DSTKG construction framework based on CEMRs was proposed. Then, this research built a knowledge graph schema containing 7 classes and 16 kinds of semantic relationships and accomplished the DSTKG by knowledge extraction, named entity linking, and drawing the knowledge graph. Finally, the quality of the DSTKG was evaluated from 3 aspects: data layer, schema layer, and application layer.
resultsExperts agreed that the DSTKG was good overall (mean score 4.20). Especially for the aspects of "rationality of schema structure," "scalability," and "readability of results," the DSTKG performed well, with scores of 4.72, 4.67, and 4.69, respectively, which were much higher than the average. However, the small amount of data in the DSTKG negatively affected its "practicability" score. Compared with other Chinese tumor knowledge graphs, the DSTKG can represent more granular entities, properties, and semantic relationships. In addition, the DSTKG was flexible, allowing personalized customization to meet the designer's focus on specific interests in the digestive system tumor.
conclusionsWe constructed a granular semantic DSTKG. It could provide guidance for the construction of a tumor knowledge graph and provide a preliminary step for the intelligent application of knowledge graphs based on CEMRs. Additional data sources and stronger research on assertion classification are needed to gain insight into the DSTKG's potential.
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