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Dementia Patient Segmentation Using EMR Data Visualization: A Design Study

Author

Listed:
  • Hyoji Ha

    (Lifemedia Interdisciplinary Program, Ajou University, Suwon 16499, Korea)

  • Jihye Lee

    (Lifemedia Interdisciplinary Program, Ajou University, Suwon 16499, Korea)

  • Hyunwoo Han

    (Lifemedia Interdisciplinary Program, Ajou University, Suwon 16499, Korea)

  • Sungyun Bae

    (Lifemedia Interdisciplinary Program, Ajou University, Suwon 16499, Korea)

  • Sangjoon Son

    (Department of Psychiatry, Ajou University School of Medicine, Suwon 16499, Korea)

  • Changhyung Hong

    (Department of Psychiatry, Ajou University School of Medicine, Suwon 16499, Korea)

  • Hyunjung Shin

    (Department of Industrial Engineering, Ajou University, Suwon 16499, Korea)

  • Kyungwon Lee

    (Department of Digital Media, Ajou University, Suwon 16499, Korea)

Abstract

(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.

Suggested Citation

  • Hyoji Ha & Jihye Lee & Hyunwoo Han & Sungyun Bae & Sangjoon Son & Changhyung Hong & Hyunjung Shin & Kyungwon Lee, 2019. "Dementia Patient Segmentation Using EMR Data Visualization: A Design Study," IJERPH, MDPI, vol. 16(18), pages 1-16, September.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:18:p:3438-:d:267786
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    References listed on IDEAS

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    1. Muhammad Noman Sohail & Jiadong Ren & Musa Uba Muhammad, 2019. "A Euclidean Group Assessment on Semi-Supervised Clustering for Healthcare Clinical Implications Based on Real-Life Data," IJERPH, MDPI, vol. 16(9), pages 1-12, May.
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