Hypothesis Generation by Interactive Visual Exploration of Heterogeneous Medical Data

Turkay, Çağatay; Lundervold, Astri J.; Hauser, Helwig

doi:10.1007/978-3-642-39146-0_1

Cited by 19 publications

(12 citation statements)

References 19 publications

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“…In healthcare, the potential of data visualization has been illustrated in a number of subareas, including patient cohort analysis. Applications span disease evolution statistics extracted from electronic medical records (EMRs) [6], [7], cohort symptom and history comparison [8], [9], [10], cohort medical image attribute comparison [11], [12], [13], and cohort heterogeneous medical data analysis [14], [15]. As often the case in clinician-driven visual analysis based on statistics, the visual encodings in these works include conventional representations such as histograms, bar charts, pie-charts, box plots, radial charts, time-series plots and scatterplots.…”

Section: Background and Related Workmentioning

confidence: 99%

Precision Risk Analysis of Cancer Therapy with Interactive Nomograms and Survival Plots

Marai

Burks

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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We present the design and evaluation of an integrated problem solving environment for cancer therapy analysis. The environment intertwines a statistical martingale model and a K Nearest Neighbor approach with visual encodings, including novel interactive nomograms, in order to compute and explain a patient's probability of survival as a function of similar patient results. A coordinated views paradigm enables exploration of the multivariate, heterogeneous and few-valued data from a large head and neck cancer repository. A visual scaffolding approach further enables users to build from familiar representations to unfamiliar ones. Evaluation with domain experts show how this visualization approach and set of streamlined workflows enable the systematic and precise analysis of a patient prognosis in the context of cohorts of similar patients. We describe the design lessons learned from this successful, multi-site remote collaboration.

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Section: Background and Related Workmentioning

confidence: 99%

Precision Risk Analysis of Cancer Therapy with Interactive Nomograms and Survival Plots

Marai

Burks

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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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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“…They analyze subsets using a drilling-down approach by incorporating scatter plot matrices (SPLOM'S) of quality metrics. Turkay et al [41] follow a similar approach by using both descriptive metrics for features as well as the features themselves and incorporate them into linked plots.…”

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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Hypothesis Generation by Interactive Visual Exploration of Heterogeneous Medical Data

Cited by 19 publications

References 19 publications

Precision Risk Analysis of Cancer Therapy with Interactive Nomograms and Survival Plots

Precision Risk Analysis of Cancer Therapy with Interactive Nomograms and Survival Plots

Hinted Star Coordinates for Mixed Data

3D Regression Heat Map Analysis of Population Study Data

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