Observatorio de I+D+i UPM

Memorias de investigación
Communications at congresses:
SNOMED CT Normal Form and HL7 RIM binding to normalize clinical data from cancer trials
Year:2013
Research Areas
  • Information technology and adata processing
Information
Abstract
Current research in oncology, require the involvement of several institutions participating in clinical trials. Heterogeneities of data formats and models require advanced methods to achieve semantic interoperability and provide sustainable solutions. In this field, the EU funded INTEGRATE project aims to develop the basic knowledge to allow data sharing of data from post-genomic clinical trials on breast cancer. In this paper, we describe the procedure implemented in this project and the required binding between relevant terminologies such as SNOMED CT and an HL7 v3 Reference Information Model (RIM)-based data model. After following the HL7 recommendations, we also describe the main issues of this process and the proposed solution, such as concept overlapping and coverage of the domain terminology. Despite the fact that the data from this domain presents a high level of heterogeneity, the methods and solutions introduced in this paper have been successfully applied within the INTEGRATE project context. Results suggest that the level of semantic interoperability required to manage patient data in modern clinical trials on breast cancer can be achieved with the proposed methodology.
International
Si
Congress
IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE)
960
Place
Reviewers
Si
ISBN/ISSN
978-1-4799-3163-7
10.1109/BIBE.2013.6701688
Start Date
10/11/2013
End Date
13/11/2013
From page
0
To page
4
2013 IEEE 13TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING (BIBE)
Participants
  • Autor: Antonio Rico Díez
  • Autor: Santiago Aso Lete (UPM)
  • Autor: David Perez Del Rey (UPM)
  • Autor: Raul Alonso Calvo (UPM)
  • Autor: Victor Manuel Maojo Garcia (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Grupo de Informática Biomédica (GIB)
  • Departamento: Inteligencia Artificial
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