| Literature DB >> 30942764 |
Parisis Gallos1, Santiago Aso2, Serge Autexier3, Arturo Brotons4, Antonio De Nigro5, Gregor Jurak6, Athanasios Kiourtis7, Pavlos Kranas8, Dimosthenis Kyriazis7, Mitja Lustrek9, Andrianna Magdalinou1, Ilias Maglogiannis7, John Mantas1, Antonio Martinez10, Andreas Menychtas11, Lydia Montandon2, Florin Picioroaga12, Manuel Perez2, Dalibor Stanimirovic13, Gregor Starc6, Tanja Tomson14, Ruth Vilar-Mateo10, Ana-Maria Vizitiu12.
Abstract
The aim of this paper is to present examples of big data techniques that can be applied on Holistic Health Records (HHR) in the context of the CrowdHEALTH project. Real-time big data analytics can be performed on the stored data (i.e. HHRs) enabling correlations and extraction of situational factors between laboratory exams, physical activities, biosignals, medical data patterns, and clinical assessment. Based on the outcomes of different analytics (e.g. risk analysis, pathways mining, forecasting and causal analysis) on the aforementioned HHRs datasets, actionable information can be obtained for the development of efficient health plans and public health policies.Keywords: Big data; health analytics; public health policy making
Mesh：
Year: 2019 PMID： 30942764
Source DB: PubMed Journal: Stud Health Technol Inform ISSN： 0926-9630