Name
Abstract | ||
|---|---|---|
Epidemiology characterizes the influence of causes to disease and health conditions of defined populations. Cohort studies are population-based studies involving usually large numbers of randomly selected individuals and comprising numerous attributes, ranging from self-reported interview data to results from various medical examinations, e.g., blood and urine samples. Since recently, medical imaging has been used as an additional instrument to assess risk factors and potential prognostic information. In this chapter, we discuss such studies and how the evaluation may benefit from visual analytics. Cluster analysis to define groups, reliable image analysis of organs in medical imaging data and shape space exploration to characterize anatomical shapes are among the visual analytics tools that may enable epidemiologists to fully exploit the potential of their huge and complex data. To gain acceptance, visual analytics tools need to complement more classical epidemiologic tools, primarily hypothesis-driven statistical analysis. |
Year | Venue | Field |
|---|---|---|
2015 | Visualization in Medicine and Life Sciences III | Data science,Population,Disease,Information visualization,Computer science,Medical imaging,Epidemiology,Visual analytics,Exploit,Cohort study |
DocType | Volume | Citations |
Journal | abs/1501.04009 | 2 |
PageRank | References | Authors |
0.38 | 20 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bernhard Preim | 1 | 1766 | 235.86 |
| Paul Klemm | 2 | 14 | 2.36 |
| Helwig Hauser | 3 | 2757 | 155.37 |
| Katrin Hegenscheid | 4 | 42 | 5.74 |
| Steffen Oeltze | 5 | 164 | 14.73 |
| Klaus D. Tönnies | 6 | 215 | 44.39 |
| Henry Völzke | 7 | 96 | 15.16 |