Designing a collaborative visual analytics system to support users’ continuous analytical processes

Jeong, Dong Hyun; Ji, Soo Yeon; Suma, Evan A.; Yu, Byunggu; Chang, Remco

doi:10.1186/s13673-015-0023-4

Cited by 15 publications

(18 citation statements)

References 24 publications

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“…Numerous useful visual analytics tools have been designed to help domain experts solve analytical problems. For example, Dong et al [37] designed a collaborative touchtable application (iPCA-CE) by adopting an existing single-user desktop analytical tool (iPCA). With the system, users can actively transit from individual desktop to shared collaborative environments without losing track of their analysis.…”

Section: Co-located Collaborative Visualizationsmentioning

confidence: 99%

Collaborative behavior, performance and engagement with visual analytics tasks using mobile devices

Chen

et al. 2020

Hum. Cent. Comput. Inf. Sci.

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Interactive visualizations are external tools that can support users’ exploratory activities. Collaboration can bring benefits to the exploration of visual representations or visualizations. This research investigates the use of co-located collaborative visualizations in mobile devices, how users working with two different modes of interaction and view (Shared or Non-Shared) and how being placed at various position arrangements (Corner-to-Corner, Face-to-Face, and Side-by-Side) affect their knowledge acquisition, engagement level, and learning efficiency. A user study is conducted with 60 participants divided into 6 groups (2 modes $$\times$$ × 3 positions) using a tool that we developed to support the exploration of 3D visual structures in a collaborative manner. Our results show that the shared control and view version in the Side-by-Side position is the most favorable and can improve task efficiency. In this paper, we present the results and a set of recommendations that are derived from them.

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Section: Co-located Collaborative Visualizationsmentioning

confidence: 99%

Collaborative behavior, performance and engagement with visual analytics tasks using mobile devices

Chen

et al. 2020

Hum. Cent. Comput. Inf. Sci.

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“…Visual Analytics (VA) systems combine the strengths of data analysis and interactive visualizations to enable users to apply filters and explore and manipulate the data inter-actively to accomplish their goals [41]. The processing load in VA is distributed between users and the key components of the system-namely, the analytics module and interactive visualization module [21,29,[42][43][44][45].…”

Section: Visual Analyticsmentioning

confidence: 99%

VERONICA: Visual Analytics for Identifying Feature Groups in Disease Classification

et al. 2021

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The use of data analysis techniques in electronic health records (EHRs) offers great promise in improving predictive risk modeling. Although useful, these analysis techniques often suffer from a lack of interpretability and transparency, especially when the data is high-dimensional. The emergence of a type of computational system known as visual analytics has the potential to address these issues by integrating data analysis techniques with interactive visualizations. This paper introduces a visual analytics system called VERONICA that utilizes the natural classification of features in EHRs to identify the group of features with the strongest predictive power. VERONICA incorporates a representative set of supervised machine learning techniques—namely, classification and regression tree, C5.0, random forest, support vector machines, and naive Bayes to support users in developing predictive models using EHRs. It then makes the analytics results accessible through an interactive visual interface. By integrating different sampling strategies, analytics algorithms, visualization techniques, and human-data interaction, VERONICA assists users in comparing prediction models in a systematic way. To demonstrate the usefulness and utility of our proposed system, we use the clinical dataset stored at ICES to identify the best representative feature groups in detecting patients who are at high risk of developing acute kidney injury.

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“…The information processing load in a VA system is distributed between the user and the main components of the VA system-namely, the analytics and the interactive visualization modules [65][66][67][68][69][70]. The data analytics module encompasses the computing space and deals with the analysis of data from the information space.…”

Section: Modules Of Visual Analytics Systemsmentioning

confidence: 99%

Multiple Regression Analysis and Frequent Itemset Mining of Electronic Medical Records: A Visual Analytics Approach Using VISA_M3R3

Abdullah

Rostamzadeh

Sedig

et al. 2020

Data

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Medication-induced acute kidney injury (AKI) is a well-known problem in clinical medicine. This paper reports the first development of a visual analytics (VA) system that examines how different medications associate with AKI. In this paper, we introduce and describe VISA_M3R3, a VA system designed to assist healthcare researchers in identifying medications and medication combinations that associate with a higher risk of AKI using electronic medical records (EMRs). By integrating multiple regression models, frequent itemset mining, data visualization, and human-data interaction mechanisms, VISA_M3R3 allows users to explore complex relationships between medications and AKI in such a way that would be difficult or sometimes even impossible without the help of a VA system. Through an analysis of 595 medications using VISA_M3R3, we have identified 55 AKI-inducing medications, 24,212 frequent medication groups, and 78 medication groups that are associated with AKI. The purpose of this paper is to demonstrate the usefulness of VISA_M3R3 in the investigation of medication-induced AKI in particular and other clinical problems in general. Furthermore, this research highlights what needs to be considered in the future when designing VA systems that are intended to support gaining novel and deep insights into massive existing EMRs.

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Designing a collaborative visual analytics system to support users’ continuous analytical processes

Cited by 15 publications

References 24 publications

Collaborative behavior, performance and engagement with visual analytics tasks using mobile devices

Collaborative behavior, performance and engagement with visual analytics tasks using mobile devices

VERONICA: Visual Analytics for Identifying Feature Groups in Disease Classification

Multiple Regression Analysis and Frequent Itemset Mining of Electronic Medical Records: A Visual Analytics Approach Using VISA_M3R3

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