| dc.contributor.author | Preim, Bernhard | en_US |
| dc.contributor.author | Alemzadeh, Shiva | en_US |
| dc.contributor.author | Ittermann, Till | en_US |
| dc.contributor.author | Klemm, Paul | en_US |
| dc.contributor.author | Niemann, Uli | en_US |
| dc.contributor.author | Spiliopoulou, Myra | en_US |
| dc.contributor.editor | Bruckner, Stefan and Oeltze-Jafra, Steffen | en_US |
| dc.date.accessioned | 2019-05-05T17:45:22Z | |
| dc.date.available | 2019-05-05T17:45:22Z | |
| dc.date.issued | 2019 | |
| dc.identifier.issn | 1017-4656 | |
| dc.identifier.uri | https://doi.org/10.2312/egm.20191034 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/egm20191034 | |
| dc.description.abstract | We present visual analytics methods to analyze epidemiologic cohort studies. We consider the automatic identification of strong correlations and of subgroups that deviate from the global mean with respect to their risk for health disorders. Moreover, we tackle missing value problems and discuss appropriate imputation strategies and visual analytics support. | en_US |
| dc.publisher | The Eurographics Association | en_US |
| dc.subject | J.3 [Computer Graphics] | |
| dc.subject | Computer Applications | |
| dc.title | Visual Analytics for Epidemiology | en_US |
| dc.description.seriesinformation | Eurographics 2019 - Dirk Bartz Prize | |
| dc.description.sectionheaders | 3rd Prize | |
| dc.identifier.doi | 10.2312/egm.20191034 | |
| dc.identifier.pages | 9-12 | |
