Dementia Patient Segmentation Using EMR Data Visualization: A Design Study

Hyoji Ha, Jihye Lee, Hyunwoo Han, Sungyun Bae, Sangjoon Son, Changhyung Hong, Hyunjung Shin and Kyungwon Lee
Additional contact information
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

IJERPH, 2019, vol. 16, issue 18, 1-16

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.

Keywords: digital health; dementia; bioinformatics; multidimensional data visualization; visual analytics; design studies; big data (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/16/18/3438/pdf (application/pdf)
https://www.mdpi.com/1660-4601/16/18/3438/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:16:y:2019:i:18:p:3438-:d:267786

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().