title: Semantic Normalization and Query Abstraction Based on SNOMED-CT and HL7: Supporting Multicentric Clinical Trials.
abstract: Advances in the use of omic data and other biomarkers are increasing the number of variables in clinical research. Additional data have stratified the population of patients and require that current studies be performed among multiple institutions. Semantic interoperability and standardized data representation are a crucial task in the management of modern clinical trials. In the past few years, different efforts have focused on integrating biomedical information. Due to the complexity of this domain and the specific requirements of clinical research, the majority of data integration tasks are still performed manually. This paper presents a semantic normalization process and a query abstraction mechanism to facilitate data integration and retrieval. A process based on well-established standards from the biomedical domain and the latest semantic web technologies has been developed. Methods proposed in this paper have been tested within the EURECA EU research project, where clinical scenarios require the extraction of semantic knowledge from biomedical vocabularies. The aim of this paper is to provide a novel method to abstract from the data model and query syntax. The proposed approach has been compared with other initiatives in the field by storing the same dataset with each of those solutions. Results show an extended functionality and query capabilities at the cost of slightly worse performance in query execution. Implementations in real settings have shown that following this approach, usable interfaces can be developed to exploit clinical trial data outcomes.
authors.forename: Sergio
authors.initials: S
authors.lastname: Paraiso-Medina
authors.forename: David
authors.initials: D
authors.lastname: Perez-Rey
authors.forename: Anca
authors.initials: A
authors.lastname: Bucur
authors.forename: Brecht
authors.initials: B
authors.lastname: Claerhout
authors.forename: Raul
authors.initials: R
authors.lastname: Alonso-Calvo
mesh_terms: D000043:Abstracting and Indexing / Q000592:standards*; D002986:Clinical Trials as Topic; D057286:Electronic Health Records; D006801:Humans; D039061:Systematized Nomenclature of Medicine