Memorias de investigación
Ponencias en congresos:
SNOMED CT Normal Form and HL7 RIM binding to normalize clinical data from cancer trials
Año:2013

Áreas de investigación
  • Ciencias de la computación y tecnología informática

Datos
Descripción
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.
Internacional
Si
Nombre congreso
IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE)
Tipo de participación
960
Lugar del congreso
Revisores
Si
ISBN o ISSN
978-1-4799-3163-7
DOI
10.1109/BIBE.2013.6701688
Fecha inicio congreso
10/11/2013
Fecha fin congreso
13/11/2013
Desde la página
0
Hasta la página
4
Título de las actas
2013 IEEE 13TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING (BIBE)

Esta actividad pertenece a memorias de investigación

Participantes

Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Grupo de Informática Biomédica (GIB)
  • Departamento: Inteligencia Artificial