Interactive Visual Analysis of Heterogeneous Cohort-Study Data

Angelelli, Paolo; Oeltze, Steffen; Haász, Judit; Turkay, Çağatay; Hodneland, Erlend; Lundervold, Astri J.; Preim, Bernhard; Hauser, Helwig

doi:10.1109/mcg.2014.40

Cited by 25 publications

(26 citation statements)

References 13 publications

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“…The individual views in dashboards and MCVs often comprise scatterplots, partially enhanced with regression lines [AOH*14, SMvB*10]. Scatterplots and scatterplot matrices often display two classes, e.g.…”

Section: Commonly Used Visual Analytics Techniquesmentioning

confidence: 99%

“…Using the Norwegian cognitive ageing study, Angellini et al . [AOH*14] describe a visual analytics system (see Figure ) that enables PH academics to efficiently test hypotheses and eventually generate new hypotheses. The support for ‘open‐ended exploration’ is considered the major requirement.…”

Section: Visual Analytics For Epidemiological Researchmentioning

confidence: 99%

“…The scatterplot (left) shows the age‐related decline of white matter integrity for the entire brain. The spatial view displays the brain and representatives for each white matter tract extracted from diffusion tensor imaging (from: [AOH*14]).…”

Section: Visual Analytics For Epidemiological Researchmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Visual Analytics for Public Health

Preim

Lawonn²

2019

Computer Graphics Forum

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We describe visual analytics solutions aiming to support public health professionals, and thus, preventive measures. Prevention aims at advocating behaviour and policy changes likely to improve human health. Public health strives to limit the outbreak of acute diseases as well as the reduction of chronic diseases and injuries. For this purpose, data are collected to identify trends in human health, to derive hypotheses, e.g. related to risk factors, and to get insights in the data and the underlying phenomena. Most public health data have a temporal character. Moreover, the spatial character, e.g. spatial clustering of diseases, needs to be considered for decision‐making. Visual analytics techniques involve (subspace) clustering, interaction techniques to identify relevant subpopulations, e.g. being particularly vulnerable to diseases, imputation of missing values, visual queries as well as visualization and interaction techniques for spatio‐temporal data. We describe requirements, tasks and visual analytics techniques that are widely used in public health before going into detail with respect to applications. These include outbreak surveillance and epidemiology research, e.g. cancer epidemiology. We classify the solutions based on the visual analytics techniques employed. We also discuss gaps in the current state of the art and resulting research opportunities in a research agenda to advance visual analytics support in public health.

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Section: Commonly Used Visual Analytics Techniquesmentioning

confidence: 99%

Section: Visual Analytics For Epidemiological Researchmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Visual Analytics for Public Health

Preim

Lawonn²

2019

Computer Graphics Forum

Self Cite

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“…Inversely, numerical values can be transformed to categorical values via binning which could be interactively modified [KBH06]. Another popular approach is the use of coordinated views, where numerical and categorical subspaces are visualized in different views [TLLH13, SSL*12, BSW*14, AOH*14]. As opposed to unification methods, these methods do not present the data in a single holistic view, and are typically application‐specific.…”

Section: Related Workmentioning

confidence: 99%

Hinted Star Coordinates for Mixed Data

Matute

Linsen

2019

Computer Graphics Forum

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Mixed data sets containing numerical and categorical attributes are nowadays ubiquitous. Converting them to one attribute type may lead to a loss of information. We present an approach for handling numerical and categorical attributes in a holistic view. For data sets with many attributes, dimensionality reduction (DR) methods can help to generate visual representations involving all attributes. While automatic DR for mixed data sets is possible using weighted combinations, the impact of each attribute on the resulting projection is difficult to measure. Interactive support allows the user to understand the impact of data dimensions in the formation of patterns. Star Coordinates is a well‐known interactive linear DR technique for multi‐dimensional numerical data sets. We propose to extend Star Coordinates and its initial configuration schemes to mixed data sets. In conjunction with analysing numerical attributes, our extension allows for exploring the impact of categorical dimensions and individual categories on the structure of the entire data set. The main challenge when interacting with Star Coordinates is typically to find a good configuration of the attribute axes. We propose a guided mixed data analysis based on maximizing projection quality measures by the use of recommended transformations, named hints, in order to find a proper configuration of the attribute axes.

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“…We incorporate their requirements, which consist of a flexible and iterative analysis. Angelelli et al [4] visualize image-derived an nonimage data using cube data structures with focus on comparison and knowledge extraction. They use Pearsson's r to characterize relationships with target features and employ list views and scatter plots to visualize and rank them.…”

Section: Prior and Related Workmentioning

confidence: 99%

3D Regression Heat Map Analysis of Population Study Data

Klemm

Lawonn

Glaser

et al. 2016

IEEE Trans. Visual. Comput. Graphics

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Epidemiological studies comprise heterogeneous data about a subject group to define disease-specific risk factors. These data contain information (features) about a subject's lifestyle, medical status as well as medical image data. Statistical regression analysis is used to evaluate these features and to identify feature combinations indicating a disease (the target feature). We propose an analysis approach of epidemiological data sets by incorporating all features in an exhaustive regression-based analysis. This approach combines all independent features w.r.t. a target feature. It provides a visualization that reveals insights into the data by highlighting relationships. The 3D Regression Heat Map, a novel 3D visual encoding, acts as an overview of the whole data set. It shows all combinations of two to three independent features with a specific target disease. Slicing through the 3D Regression Heat Map allows for the detailed analysis of the underlying relationships. Expert knowledge about disease-specific hypotheses can be included into the analysis by adjusting the regression model formulas. Furthermore, the influences of features can be assessed using a difference view comparing different calculation results. We applied our 3D Regression Heat Map method to a hepatic steatosis data set to reproduce results from a data mining-driven analysis. A qualitative analysis was conducted on a breast density data set. We were able to derive new hypotheses about relations between breast density and breast lesions with breast cancer. With the 3D Regression Heat Map, we present a visual overview of epidemiological data that allows for the first time an interactive regression-based analysis of large feature sets with respect to a disease.

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Interactive Visual Analysis of Heterogeneous Cohort-Study Data

Cited by 25 publications

References 13 publications

A Survey of Visual Analytics for Public Health

A Survey of Visual Analytics for Public Health

Hinted Star Coordinates for Mixed Data

3D Regression Heat Map Analysis of Population Study Data

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