Name
Abstract | ||
|---|---|---|
Among the basic research tools for (bio)medical science are epidemiological studies that typically involve a number of hospitals, clinics, and research centres scattered around the world, and are often referred to as multi-centre studies. Clearly, the effectiveness and importance of a multi-centre study increases with the number of participating centres and enrolled patients, but at the same time this natural distribution in the production of research data requires sophisticated data/knowledge management infrastructures to support the participating units. This kind of infrastructure is not only expensive to build and maintain, but also cannot be reused as it is often tailored to a specific study. In this work, we present a cloud-based system, that allows users without any computer science background to design, deploy, and administer platforms aimed for managing, sharing, and analysing clinical data from multi-centre studies. The proposed system provides a zero-administration, zero-cost online data/knowledge management tool that (i) enhances re-usability by introducing study templates, (ii) supports (bio)medical needs through specialised data types able to capture specialised knowledge like repeated therapies or treatments, and (iii) emphasises data filtering/export through an expressive yet simple graphical query engine.
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Year | Venue | Field |
|---|---|---|
2015 | K-CAP | Data mining,Data filtering,Computer science,Knowledge management,Description logic,Data type,Basic research,Utility theory,Cloud computing |
DocType | ISBN | Citations |
Conference | 978-1-4503-3849-3 | 0 |
PageRank | References | Authors |
0.34 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amalia Tsafara | 1 | 0 | 0.34 |
| Christos Tryfonopoulos | 2 | 246 | 21.99 |
| Spiros Skiadopoulos | 3 | 1139 | 65.60 |
| Lefteris Zervakis | 4 | 0 | 2.03 |