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Case-Reported Data Management Methodology Using an RDF Data Model for Building a Multicenter Clinical Registry
Masamichi Ishii1, Hiroyuki Hoshimoto1, Kengo Miyo1
1Center for Medical Informatics Intelligence, National Center for Global Health and Medicine, Tokyo, Japan.
Managing clinical data mapping between institutional and standardized codes is crucial for research. Resource Description Framework (RDF) and SPARQL APIs enable flexible, knowledge-based data management.
Area of Science:
- Clinical Informatics
- Data Management
- Semantic Web Technologies
Background:
- Multicenter clinical research generates complex case-reported data.
- Institutions use in-house codes, requiring mapping to standardized codes for data integration.
- Semantic hierarchy gaps between electronic medical records and case report forms pose challenges.
Purpose of the Study:
- To develop a method for managing mapping information between in-house and standardized codes.
- To enable flexible and efficient data management in multicenter clinical research.
- To leverage Resource Description Framework (RDF) and SPARQL for knowledge-based data integration.
Main Methods:
- Centralized management of mapping information in RDF stores.
- Description of relationships between standardized and in-house codes using RDF triples.
- Development and verification of RESTful APIs for accessing RDF data via SPARQL.
Main Results:
- Demonstrated the feasibility of data management using knowledge bases represented as RDF graphs.
- Expressed the relationship between standardized and in-house pharmaceutical codes in RDF triples.
- Confirmed that implemented APIs allow dynamic modification of mapping definitions.
Conclusions:
- RDF-based knowledge graphs provide a flexible framework for managing complex code mappings.
- RESTful APIs facilitate efficient access and dynamic updates to mapping data.
- This approach enhances data interoperability and reduces operational restrictions in clinical research.
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