| Literature DB >> 28679877 |
Dimosthenis Kyriazis1, Serge Autexier2, Iván Brondino3, Michael Boniface4, Lucas Donat5, Vegard Engen4, Rafael Fernandez6, Ricardo Jimenez-Peris3, Blanca Jordan7, Gregor Jurak8, Athanasios Kiourtis1, Thanos Kosmidis9, Mitja Lustrek10, Ilias Maglogiannis1, John Mantas11, Antonio Martinez5, Argyro Mavrogiorgou1, Andreas Menychtas12, Lydia Montandon7, Cosmin-Septimiu Nechifor13, Sokratis Nifakos14, Alexandra Papageorgiou15, Marta Patino-Martinez6, Manuel Perez7, Vassilis Plagianakos15, Dalibor Stanimirovic16, Gregor Starc8, Tanja Tomson14, Francesco Torelli17, Vicente Traver-Salcedo5, George Vassilacopoulos1, Usman Wajid18.
Abstract
Today's rich digital information environment is characterized by the multitude of data sources providing information that has not yet reached its full potential in eHealth. The aim of the presented approach, namely CrowdHEALTH, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants. HHRs are transformed into HHRs clusters capturing the clinical, social and human context of population segments and as a result collective knowledge for different factors. The proposed approach also seamlessly integrates big data technologies across the complete data path, providing of Data as a Service (DaaS) to the health ecosystem stakeholders, as well as to policy makers towards a "health in all policies" approach. Cross-domain co-creation of policies is feasible through a rich toolkit, being provided on top of the DaaS, incorporating mechanisms for causal and risk analysis, and for the compilation of predictions.Entities:
Keywords: Big data; disease prevention; health analytics; health promotion; public health policy making
Mesh：
Year: 2017 PMID： 28679877
Source DB: PubMed Journal: Stud Health Technol Inform ISSN： 0926-9630