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Article

Dementia Patient Segmentation Using EMR Data Visualization: A Design Study

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Lifemedia Interdisciplinary Program, Ajou University, Suwon 16499, Korea
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Department of Psychiatry, Ajou University School of Medicine, Suwon 16499, Korea
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Department of Industrial Engineering, Ajou University, Suwon 16499, Korea
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Department of Digital Media, Ajou University, Suwon 16499, Korea
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Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2019, 16(18), 3438; https://doi.org/10.3390/ijerph16183438
Received: 1 August 2019 / Revised: 31 August 2019 / Accepted: 7 September 2019 / Published: 16 September 2019
(This article belongs to the Special Issue The Future of Healthcare: Telemedicine, Public eHealth, and Big Data)
(1) Background: The Electronic Medical Record system, which is a digital medical record management architecture, is critical for reliable medical research. It facilitates the investigation of disease patterns and efficient treatment via collaboration with data scientists. (2) Methods: In this study, we present multidimensional visual tools for the analysis of multidimensional datasets via a combination of 3-dimensional radial coordinate visualization (3D RadVis) and many-objective optimization (e.g., Parallel Coordinates). Also, we propose a user-driven research design to facilitate visualization. We followed a design process to (1) understand the demands of domain experts, (2) define the problems based on relevant works, (3) design visualization, (4) implement visualization, and (5) enable qualitative evaluation by domain experts. (3) Results: This study provides clinical insight into dementia based on EMR data via visual analysis. Results of a case study based on questionnaires surveying daily living activities indicated that daily behaviors influenced the progression of dementia. (4) Conclusions: This study provides a visual analytical tool to support cluster segmentation. Using this tool, we segmented dementia patients into clusters and interpreted the behavioral patterns of each group. This study contributes to biomedical data interpretation based on a visual approach. View Full-Text
Keywords: digital health; dementia; bioinformatics; multidimensional data visualization; visual analytics; design studies; big data digital health; dementia; bioinformatics; multidimensional data visualization; visual analytics; design studies; big data
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MDPI and ACS Style

Ha, H.; Lee, J.; Han, H.; Bae, S.; Son, S.; Hong, C.; Shin, H.; Lee, K. Dementia Patient Segmentation Using EMR Data Visualization: A Design Study. Int. J. Environ. Res. Public Health 2019, 16, 3438. https://doi.org/10.3390/ijerph16183438

AMA Style

Ha H, Lee J, Han H, Bae S, Son S, Hong C, Shin H, Lee K. Dementia Patient Segmentation Using EMR Data Visualization: A Design Study. International Journal of Environmental Research and Public Health. 2019; 16(18):3438. https://doi.org/10.3390/ijerph16183438

Chicago/Turabian Style

Ha, Hyoji, Jihye Lee, Hyunwoo Han, Sungyun Bae, Sangjoon Son, Changhyung Hong, Hyunjung Shin, and Kyungwon Lee. 2019. "Dementia Patient Segmentation Using EMR Data Visualization: A Design Study" International Journal of Environmental Research and Public Health 16, no. 18: 3438. https://doi.org/10.3390/ijerph16183438

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