Combining Visual Cleansing and Exploration for Clinical Data

Schmidt, Christoph; Röhlig, Martin; Grundel, Bastian; Daumke, Philipp; Ritter, Marc; Stahl, Andreas; Rosenthal, Paul; Schumann, Heidrun

doi:10.1109/vahc47919.2019.8945034

Cited by 5 publications

(8 citation statements)

References 42 publications

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“…Visual Analysis of healthcare data is an active area of research. Event-based visual analytics approaches for health record data has been a main focus of this area [13,26,28,41,53,54,57]. Disease surveillance and epidemiology has also been of interest [49].…”

Section: Related Workmentioning

confidence: 99%

In Search of Patient Zero: Visual Analytics of Pathogen Transmission Pathways in Hospitals

Baumgartl

Petzold

Wunderlich

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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Section: Related Workmentioning

confidence: 99%

In Search of Patient Zero: Visual Analytics of Pathogen Transmission Pathways in Hospitals

Baumgartl

Petzold

Wunderlich

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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“…Also, the data preprocessing step (Schmidt et al [Sch+19]) and data cleansing step (McCurdy et al [MGM18]) are supported by annotations. Despite the fact, that literature shows various work on annotations supporting steps in the analysis, work on several steps at once is still challenging.…”

Section: Related Workmentioning

confidence: 99%

“…We chose these steps, as previous analyses in the field of heterogeneous real-world data have identified them as important (e.g. [Gsc+12], [Sch+19])…”

Section: Annotation Description For the Analysis Stepsmentioning

confidence: 99%

“…Data preprocessing, data cleansing, and data exploration are common steps in visual analytics processes [Sac+17], [Sch+19], [Gsc+12]. Each of these steps has its own challenges.…”

Section: Introductionmentioning

confidence: 99%

“…With these annotations at hand, we extend an existing visual analytics tool (from [Sch+19]), allowing the gathering, processing, and communication of additional information that assists users during their visual analytics process. We apply the extended tool on a use case involving heterogeneous, contradictory, and incomplete data from the medical domain.…”

Section: Introductionmentioning

confidence: 99%

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Varying Annotations in the Steps of the Visual Analysis

Schmidt,

Rosenthal,

Schumann

2020

Preprint

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Annotations in Visual Analytics (VA) have become a common means to support the analysis by integrating additional information into the VA system. That additional information often depends on the current process step in the visual analysis. For example, the data preprocessing step has data structuring operations while the data exploration step focuses on user interaction and input. Describing suitable annotations to meet the goals of the different steps is challenging. To tackle this issue, we identify individual annotations for each step and outline their gathering and design properties for the visual analysis of heterogeneous clinical data. We integrate our annotation design into a visual analysis tool to show its applicability to data from the ophthalmic domain. In interviews and application sessions with experts we asses its usefulness for the analysis of patients with different medications.

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