Iterative cohort analysis and exploration

Zhang, Zhiyuan; Gotz, David; Perer, Adam

doi:10.1177/1473871614526077

Cited by 52 publications

(40 citation statements)

References 36 publications

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“…They investigated the combination of clustering analysis and visualization. Zhang et al [ZGP15] provide CAVA, a framework for the visual analysis of cohort study data. The CAVA system lets the analyst build groups of patients for further investigations by visual queries.…”

Section: Visual Analytics Of Cohort Study Datamentioning

confidence: 99%

Visual Analysis of Missing Values in Longitudinal Cohort Study Data

Alemzadeh

Niemann

Ittermann

et al. 2019

Computer Graphics Forum

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Attrition or dropout is the most severe missingness problem in longitudinal cohort study data where some participants do not show up for follow‐up examinations. Dropouts result in biased data and cause the reduction of 1ata set size. Moreover, they limit the power of statistical analysis and the validity of study findings. Visualization can play a strong role in analysing and displaying the missingness patterns. In this work, we present VIVID, a framework for the visual analysis of missing values in cohort study data. VIVID is inspired by discussions with epidemiologists and adds visual components to their current statistics‐based approaches. VIVID provides functions for exploration, imputation and validity check of imputations. The main focus of this paper is multiple imputation to fix the missing data.

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Section: Visual Analytics Of Cohort Study Datamentioning

confidence: 99%

Visual Analysis of Missing Values in Longitudinal Cohort Study Data

Alemzadeh

Niemann

Ittermann

et al. 2019

Computer Graphics Forum

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“…The work of Zhang et al [12,43] is closest to ours regarding the application of a visual analysis of population study data. They present Cohort Analysis via Visual Analytics (CAVA), a framework that distinguishes three major elements of a cohort study data analysis: cohort data (and its manipulation using operations), views and analytics.…”

Section: Prior and Related Workmentioning

confidence: 97%

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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“…Query-based techniques [25], [52], [103], [114], [127] enable analysts to create complex queries to extract event sequences of interest. For instances, in COQUITO [52] and CAVA [127], analysts can express complex queries for iterative cohort construction. Mining-based techniques leverage advanced sequential pattern mining algorithms to extract insights from complex event sequences [56], [59], [60], [61], [76], [77].…”

Section: Visual Summarizationmentioning

confidence: 99%

Visual Progression Analysis of Event Sequence Data

Guo

Jin

Gotz

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Event sequence data is common to a broad range of application domains, from security to health care to scholarly communication. This form of data captures information about the progression of events for an individual entity (e.g., a computer network device; a patient; an author) in the form of a series of time-stamped observations. Moreover, each event is associated with an event type (e.g., a computer login attempt, or a hospital discharge). Analyses of event sequence data have been shown to help reveal important temporal patterns, such as clinical paths resulting in improved outcomes, or an understanding of common career trajectories for scholars. Moreover, recent research has demonstrated a variety of techniques designed to overcome methodological challenges such as large volumes of data and high dimensionality. However, the effective identification and analysis of latent stages of progression, which can allow for variation within different but similarly evolving event sequences, remain a significant challenge with important real-world motivations. In this paper, we propose an unsupervised stage analysis algorithm to identify semantically meaningful progression stages as well as the critical events which help define those stages. The algorithm follows three key steps: (1) event representation estimation, (2) event sequence warping and alignment, and (3) sequence segmentation. We also present a novel visualization system, ET2, which interactively illustrates the results of the stage analysis algorithm to help reveal evolution patterns across stages. Finally, we report three forms of evaluation for ET2: (1) case studies with two real-world datasets, (2) interviews with domain expert users, and (3) a performance evaluation on the progression analysis algorithm and the visualization design.

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Iterative cohort analysis and exploration

Cited by 52 publications

References 36 publications

Visual Analysis of Missing Values in Longitudinal Cohort Study Data

Visual Analysis of Missing Values in Longitudinal Cohort Study Data

3D Regression Heat Map Analysis of Population Study Data

Visual Progression Analysis of Event Sequence Data

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