An ontological modeling approach to cerebrovascular disease studies: The NEUROWEB case
The NEUROWEB project supports cerebrovascular researchers' association studies, intended as the search for statistical correlations between a feature (e.g., a genotype) and a phenotype. In this project the phenotype refers to the patients' pathological state, and thus it is formulated on the basis of the clinical data collected during the diagnostic activity. In order to enhance the statistical robustness of the association inquiries, the project involves four European Union clinical institutions. Each institution provides its proprietary repository, storing patients' data. Although all sites comply with common diagnostic guidelines, they also adopt specific protocols, resulting in partially discrepant repository contents. Therefore, in order to effectively exploit NEUROWEB data for association studies, it is necessary to provide a framework for the phenotype formulation, grounded on the clinical repository content which explicitly addresses the inherent integration problem. To that end, we developed an ontological model for cerebrovascular phenotypes, the NEUROWEB Reference Ontology, composed of three layers. The top-layer (Top Phenotypes) is an expert-based cerebrovascular disease taxonomy. The middle-layer deconstructs the Top Phenotypes into more elementary phenotypes (Low Phenotypes) and general-use medical concepts such as anatomical parts and topological concepts. The bottom-layer (Core Data Set, or CDS) comprises the clinical indicators required for cerebrovascular disorder diagnosis. Low Phenotypes are connected to the bottom-layer (CDS) by specifying what combination of CDS values is required for their existence. Finally, CDS elements are mapped to the local repositories of clinical data. The NEUROWEB system exploits the Reference Ontology to query the different repositories and to retrieve patients characterized by a common phenotype. (C) 2009 Elsevier Inc. All rights reserved.
association studies biomedical ontologies data integration clinical phenotypes