Dementia Patient Segmentation Using EMR Data Visualization: A Design Study

Ha, Hyoji; Lee, Jihye; Han, Hyunwoo; Bae, Sungyun; Son, Sangjoon; Hong, Chang-Hyung; Shin, Hyunjung; Lee, Kyungwon

doi:10.3390/ijerph16183438

Cited by 7 publications

(3 citation statements)

References 30 publications

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“…It allows clinical researchers to identify which features are more important within each cluster of patients. RadVis [73] is a VA system that enables clinicians to better understand the characteristics of patient clusters. It allows the user to apply different clustering techniques and displays the result using a 3-dimensional radial coordinate visualization.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%

VERONICA: Visual Analytics for Identifying Feature Groups in Disease Classification

et al. 2021

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“…H. Ha, et al proposed a visualization method for visual analysis of EMR data. Multidimensional medical data can be analyzed through collaboration with psychiatrists [25] [31].…”

Section: Interactive Visualization Systemmentioning

confidence: 99%

Multi-View Interactive Visual Exploration of Individual Association for Public Transportation Passengers

Zhang

Lin

et al. 2020

Applied Sciences

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More and more people in mega cities are choosing to travel by public transportation due to its convenience and punctuality. It is widely acknowledged that there may be some potential associations between passengers. Their travel behavior may be working together, shopping together, or even some abnormal behaviors, such as stealing or begging. Thus, analyzing association between passengers is very important for management departments. It is very helpful to make operational plans, provide better services to passengers and ensure public transport safety. In order to quickly explore the association between passengers, we propose a multi-view interactive exploration method that provides five interactive views: passenger 3D travel trajectory view, passenger travel time pixel matrix view, passenger origin-destination chord view, passenger travel vehicle bubble chart view and passenger 2D travel trajectory view. It can explore the associated passengers from multiple aspects such as travel trajectory, travel area, travel time, and vehicles used for travel. Using Beijing public transportation data, the experimental results verified that our method can effectively explore the association between passengers and deduce the relationship.

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“…These attribute keywords were called "dimensional anchors". The dimensional anchor refers to a point in the process where the applied strength of data instance nodes varies in accordance with their values [44,45]. Dimensional anchors are presented as points at equidistant intervals on a circumference.…”

Section: Design Process Assessment Of Visualizationmentioning

confidence: 99%

A Visualization System for Exploring Logo Trend and Design Shape Patterns

Han

Lee

2020

Applied Sciences

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A logo is an effective way of expressing a brand’s identity and an essential element in conveying the values and image of the company. The development process of a competitive logo should be based on a design that is future-proof in a rapidly changing global market; hence, understanding the design trends for successful logo design is key. In this study, the design shape elements of logo trend models were analyzed and made into a database. Then, a trend analysis system was produced using radial visualization (RadViz) and circular parallel coordinates data visualization techniques. RadViz allows observation of clusters of logos that have similar shape elements, whereas with circular parallel coordinates plots, detailed information of the shape elements of each logo trend can be seen. Using the system, it was confirmed that shape elements—such as transformation to surface, overlapping, artificiality, concept of color and rhythm—play a major role in driving a trend. It was observed that trends change over time as various shape elements are added or removed. In addition, our study is expected to help predict the logo trend models that will come into style in the future. While similar efforts have been made in the past, our proposed system improves upon them by utilizing standard design elements as the categorizing criteria, using a unique combination of RadViz and circular parallel coordinates data visualization techniques. Using our system as a guideline, many users would be able to create logos that reflect what is trending.

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Dementia Patient Segmentation Using EMR Data Visualization: A Design Study

Cited by 7 publications

References 30 publications

VERONICA: Visual Analytics for Identifying Feature Groups in Disease Classification

VERONICA: Visual Analytics for Identifying Feature Groups in Disease Classification

Multi-View Interactive Visual Exploration of Individual Association for Public Transportation Passengers

A Visualization System for Exploring Logo Trend and Design Shape Patterns

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