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Published on: May 17, 2019
Enabling semantic interoperability in multi-centric clinical trials on breast cancer
Raul Alonso-Calvo1, David Perez-Rey1, Sergio Paraiso-Medina1
1Biomedical Informatics Group, DLSIIS & DIA, Facultad de Informática, Universidad Politécnica de Madrid, Campus de Montegancedo S/N, 28660 Boadilla del Monte, Madrid, Spain.
This study introduces a standard-based solution for uniform patient data access in multi-institutional clinical trials, enhancing data integration and retrieval efficiency for research.
Area of Science:
- Biomedical Informatics
- Clinical Research Informatics
- Health Data Standards
Background:
- Post-genomic clinical trials necessitate data collection from multiple institutions, including hospitals, labs, and research facilities.
- Integrating patient data across diverse healthcare settings presents significant challenges for clinical research.
Purpose of the Study:
- To present a standard-based solution for uniform access to patient data in clinical research.
- To address the need for seamless data integration in multi-institutional post-genomic trials.
Main Methods:
- Leveraging established standards like Health Level Seven (HL7) v3 and SPARQL.
- Utilizing medical vocabularies such as Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT), Logical Observation Identifiers Names and Codes (LOINC), and HUGO Gene Nomenclature Committee (HGNC).
- Employing Semantic Web technologies for semantic normalization between HL7 data models and biomedical ontologies.
Main Results:
- Demonstrated successful semantic interoperability through various query tests.
- Achieved efficient data retrieval times, suitable for end-user tools requiring integrated data access.
- Validated the solution's performance in retrieving clinical patient data from heterogeneous sources.
Conclusions:
- The proposed solution was successfully tested within the INTEGRATE and EURECA European Union projects.
- Applications were deployed for patient screening, trial recruitment, and retrospective analysis.
- The approach enables semantically interoperable access to clinical patient data, facilitating multi-site research.
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