Marie-Christine Jaulent1, Ariane Assélé-Kama1, Sébastien Savard2, Alessandra Giavarini2, Emmanuel Touzé3, Xavier Jeunemaître2, Adrien Ugon1, Pierre-François Plouin2, Laurent Toubiana1. 1. INSERM, U1142, LIMICS, F-75006, Paris, France; Sorbonne Universités, UPMC Université Paris 06, UMR_S 1142, LIMICS, F-75006, Paris, France; Université Paris 13, Sorbonne Paris Cité, UMR_S 1142, LIMICS, F-93430, Villetaneuse, France. 2. Hypertension unit, Department of genetics and Centre for rare vascular diseases; Hopital Europeen G Pompidou (HEGP); Paris-Descartes University ; INSERM U970, Paris Cardiovascular Research Centre, 75015 Paris, France. 3. Université Caen Basse Normandie, CHU Côte-de-Nacre, service de neurologie et unité neurovasculaire, 14000 Caen, France; INSERM U919, GIP CYCERON, 14074 Caen, France.
Abstract
UNLABELLED: Identifying patients with Fibromuscular Dysplasia (FMD) at the international level will have considerable value for understanding the epidemiology, clinical manifestations and susceptible genes in this arterial disease, but also for identifying eligible patients in clinical trials or cohorts. We present a two-step methodology to create a general semantic interoperability framework allowing access and comparison of distributed data over various nations, languages, formats and databases. METHODS: The first step is to develop a pivot multidimensional model based on a core dataset to harmonize existing heterogeneous data sources. The second step is to align the model to additional data, semantically related to FMD and collected currently in various registries. We present the results of the first step that has been fully completed with the validation and implementation of the model in a dedicated information system (SIR-FMD). We discuss the current achievements for step 2 and the extensibility of the methodology in the context of other rare diseases.
UNLABELLED: Identifying patients with Fibromuscular Dysplasia (FMD) at the international level will have considerable value for understanding the epidemiology, clinical manifestations and susceptible genes in this arterial disease, but also for identifying eligible patients in clinical trials or cohorts. We present a two-step methodology to create a general semantic interoperability framework allowing access and comparison of distributed data over various nations, languages, formats and databases. METHODS: The first step is to develop a pivot multidimensional model based on a core dataset to harmonize existing heterogeneous data sources. The second step is to align the model to additional data, semantically related to FMD and collected currently in various registries. We present the results of the first step that has been fully completed with the validation and implementation of the model in a dedicated information system (SIR-FMD). We discuss the current achievements for step 2 and the extensibility of the methodology in the context of other rare diseases.