In response to the need to tackle increasingly complex medical research questions, a growing amount of human health data is being collected, either in routine Electronic Healthcare Record (EHR) databases, through research-driven c...
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Descripción del proyecto
In response to the need to tackle increasingly complex medical research questions, a growing amount of human health data is being collected, either in routine Electronic Healthcare Record (EHR) databases, through research-driven cohort studies, in biobanks or related efforts. However, data sources are typically fragmented and contain information gaps which prevent their full exploitation. EMIF aims to address this by developing a common Information Framework that enables improved access to these data sources, enhancement through linkage of the different sources and collection of additional new information. EMIF will focus on two specific research objectives in order to guide the development of the Information Framework: identification and evaluation of biomarkers i) of the risk for metabolic complications in obesity; ii) of Alzheimer’s Disease onset in the preclinical and prodromal phase, which in both cases will identify high-risk individuals for future intervention trials. To achieve this, a variety of data sources ranging from small-scale information-rich disease cohorts for biomarker discovery to large EHR data for population characterisation and biomarker validation will be utilised. An extreme phenotype approach will utilise the subpopulations at the extremes of a particular trait distribution using large-scale metabolomics and proteomics for biomarker evaluation. The development of the Information Framework will involve addressing data standards, semantic interoperability as well as ethics, data privacy, legal issues and the development of an IT platform for multi data sources access. The Information Framework will be designed to support the current research objectives, but more generally studies using human health data. The project consortium is a partnership between Academia and EFPIA and comprises a large number of world-renown experts in data access and linkage and the Metabolic and AD therapeutic areas, with many being involved in other related projects