Data visualization is a field of study that concentrates on the use of computer-supported tools to explore and represent large amount of data. Data visualization focuses on the creation of approaches for conveying abstract information in intuitive ways.Visual representations take advantage of the human eye’s broad bandwidth pathway into the mind. Practical application of data visualization in decision support involves selecting, transforming and representing data in a form that facilitates data exploration and understanding, often called knowledge discovery or visual data mining. Important aspects of data visualization are the interactivity and dynamics of visual representation. For example, CaseReasoner implements treemaps, relative neighbourhood graphs (RNGs) and heatmaps as techniques for the visualization of inter-patient similarity in clinical decision support, assisting also in clinical knowledge discovery. The importance of data visualization and visual data mining for knowledge discovery and decision support has long been underestimated; this field has a great promise of being significantly advanced in the near future and of finding a variety of successful applications in different subject domains.
Published in Chapter:
Gridifying Biomedical Applications in the Health-e-Child Project
David Manset (maat Gknowledge, France), Frederic Pourraz (maat Gknowledge, France), Alexey Tsymbal (Siemens AG, Germany), Jerome Revillard (maat Gknowledge, France), Konstantin Skaburskas (CERN, Switzerland), Richard McClatchey (University of the West of England, UK), Ashiq Anjum (University of the West of England, UK), Alfonso Rios (maat Gknowledge, Spain), and Martin Huber (Siemens AG, Germany)
Copyright: © 2009
|Pages: 25
DOI: 10.4018/978-1-60566-374-6.ch024
Abstract
The Health-e-Child project started in January 2006 with the aim of developing a Grid-based healthcare platform for European paediatrics and providing seamless integration of traditional and emerging sources of biomedical information. The objective of this chapter is to share experiences, and present major issues faced, solutions found and a roadmap for future work in developing the Grid infrastructure for interactive biomedical applications in the project, as Health-e-Child approaches its final phases. This chapter starts with a brief introduction of the project itself, followed by a description of its architecture on the Grid. It then illustrates the approach with the description of a concrete example of one integrated key application, the Health-e-Child CaseReasoner, which is intended for biomedical decision support over the Grid, and is based on similarity search and advanced data visualization techniques.