Advanced visual analytics interfaces for adverse drug event detection

Mittelstädt, Sebastian; Hao, Ming C.; Dayal, Umeshwar; Hsu, Meichun; Terdiman, Joseph; Keim, Daniel A.

doi:10.1145/2598153.2598156

Cited by 11 publications

(10 citation statements)

References 18 publications

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“…Besides statistical analysis of ''unexpectedness'' of gene functions, this also requires a tightly integrated expert for justification with visual analysis tools since ''expectedness'' depends also on implicit domain expert knowledge and is, therefore, ill-defined. A similar problem and solution was presented by Mittelstädt et al 48 that required a tightly integrated physician for adverse drug event detection.…”

Section: Limitations and Future Workmentioning

confidence: 96%

VisExpress: Visual exploration of differential gene expression data

Simon

Mittelstädt

Kwon

et al. 2016

Information Visualization

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Biologists are keen to understand how processes in cells react to environmental changes. Differential gene expression analysis allows biologists to explore functions of genes with data generated from different environments. However, these data and analysis lead to unique challenges since tasks are ill-defined, require implicit domain knowledge, comprise large volumes of data, and are, therefore, of explanatory nature. To investigate a scalable visualization-based solution, we conducted a design study with three biologists specialized in differential gene expression analysis. We stress our contributions in three aspects: first, we characterize the problem domain for exploring differential gene expression data and derive task abstractions and design requirements. Second, we investigate the design space and present an interactive visualization system, called VisExpress. Third, we evaluate the usefulness of VisExpress via a Pair Analytics study with real users and real data and report on insights that were gained by our experts with VisExpress.

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Section: Limitations and Future Workmentioning

confidence: 96%

VisExpress: Visual exploration of differential gene expression data

Simon

Mittelstädt

Kwon

et al. 2016

Information Visualization

Self Cite

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“…The ADEs are categorized with respect to severity (red denotes death of the patient). The size of the glyphs represents the frequency (from: [MHD*14]).…”

Section: Visual Analytics For Epidemiological Researchmentioning

confidence: 99%

“…between different diseases or between diseases and exposures [BDD14]. Treemaps are occasionally used to indicate the relative frequency of events [MHD*14, TRL*17]. Histograms and other summary views may also be incorporated [LRS12].…”

Section: Commonly Used Visual Analytics Techniquesmentioning

confidence: 99%

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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“…Ramírez-Ramírez et al 41 discussed a tool called SIMID (SIMulation of Infectious Diseases) providing medical decision-makers with effective spatio-temporal visualization of infectious disease spread. Mittelstädt et al 42 introduced a visual iDSS for adverse drug event detection. Fischer et al 43 proposed a visual analytics tool called NVisAware aiming at compressing data streams for visually identifying events to solve the Kronos incident problem.…”

Section: Visual Idssmentioning

confidence: 99%

Fuzzy logic–based evaluation of visualizations generated by intelligent decision support systems

Ltifi

Amri

Ayed

2016

Information Visualization

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The development of intelligent decision support systems requires much research effort to solve decision-making problems’ complexity. In fact, the combination of both intelligent components and visualization aspects in intelligent decision support system required a lot of efforts in order to develop advanced information visualization schemes for decision-making processes. For this, an efficient evaluation of these systems has become a major concern for applications in multiple fields. The reports of the existing usability evaluation studies are helpful to verify the potential and the limitations of these tools. However, it is important to integrate more relevant metrics for visual analytics tasks in dynamic intelligent decision support system. The proposed method consists of a questionnaire that is given to the users and a subsequent analysis of the resulting data using fuzzy logic. The advantage of the fuzzy model is its ability to transform the input survey scores into linguistic variables, as well as linguistic evaluation of the overall intelligent decision support system visualization tool. With this approach, it is possible to model the vagueness in the ordinal judgments obtained from the users’ evaluation about the visualizations of intelligent decision support system and to support uncertainty in such evaluation.

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Advanced visual analytics interfaces for adverse drug event detection

Cited by 11 publications

References 18 publications

VisExpress: Visual exploration of differential gene expression data

VisExpress: Visual exploration of differential gene expression data

A Survey of Visual Analytics for Public Health

Fuzzy logic–based evaluation of visualizations generated by intelligent decision support systems

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