short-paper

Visual analytics for biomedical cluster subdivision: a design study with psychiatrists

Authors:

Changhyung Hong,

Kyungwon LeeAuthors Info & Claims

CGI '17: Proceedings of the Computer Graphics International Conference

Article No.: 17, Pages 1 - 6

https://doi.org/10.1145/3095140.3095157

Published: 27 June 2017 Publication History

Abstract

In the last few years, Electronic Health Records (EHRs) have collected large-sized medical data. While EHR allows doctors to approach medication data easily, they suffer difficulties to analyze multi-dimensional medical data. We present multi-dimensional visual analytics tools to support analyzing multi-dimensional dataset by the combination of 3D RadVis and parallel coordinate. Also, we propose user-driven research design process to prospect for visualization development. This study is now in progress to interview domain experts to analyze the usability of the tools.

References

[1]

M. Ankerst, S. Berchtold, & D. A. Keim. Similarity clustering of dimensions for an enhanced visualization of multidimensional data. In proceedings of Information Visualization, (VIS 1998), North Carolina, United States, 52--60

Digital Library

[2]

M. Beham, W. Herzner, M. E. Gröller, & J Kehrer, Cupid: cluster-based exploration of geometry generators with parallel coordinates and radial trees. IEEE transactions on visualization and computer graphics, 20,12(2014), 1693--1702

[3]

E. Bertini, L. Dell'Aquila, G. Santucci. 2005. Springview: Cooperation of radviz and parallel coordinates for view optimization and clutter reduction. In Proceeding of the Coordinated and Multiple Views in Exploratory Visualization (CMV 2005). IEEE, 22--29.

Digital Library

[4]

S,H, Choi, J. Lee, S.J. Kim, J.Y. Choi, J.W. Kwon, B.N. Yoon, Y.S. Yang, S.Y Kim, J.H Jeong. 2014. Driving in Patients with Dementia: A CREDOS (Clinical Research Center for Dementia of South Korea) Study. Dementia and Neurocognitive Disorders, 13, 4 (2014), 83--88.

[5]

B. Everi, S. Landau, S. Leese, D. Stahl, 2011, Cluster Analysis, 5th Edition, 71--110. WILEY SERIES IN PROBABILITY AND STATISTICS, New Jersey, 1.5.3. Psychiatry.

[6]

Y. H. Fua, M. O. Ward, & E. A. Rundensteiner, Hierarchical parallel coordinates for exploration of large datasets. In Proceedings of the conference on Visualization'99 (VIS 1999), California, United States, 43--50.

Digital Library

[7]

S Ghani, B. C. Kwon, S. Lee, J. S. Yi, & N. Elmqvist, Visual analytics for multimodal social network analysis: A design study with social scientists. IEEE Transactions on Visualization and Computer Graphics, 19,12, (2013), 2032--2041

Digital Library

[8]

H. Ha, H. Han, S. Bae, J. Lee, S. Son, C. Hong, H. Shin, K. Lee, A Study on Visualization Methods of Semantic Clustering for Multidimensional data, Communications of the Korean Institute of Information Scientists and Engineers, 11,34, (2016),51--61.

[9]

P. Hoffman, G. Grinstein, K. Marx, I. Grosse, & E. Stanley, DNA visual and analytic data mining. In Proceedings of Visualization'97(VIS 1997), Arizona, United States, 437--441.

Digital Library

[10]

A. Ibrahim, S. Rahnamayan, M. V. Martin, & Deb, K. Deb, 3D-RadVis: Visualization of Pareto front in many-objective optimization. In proceding of Evolutionary Computation (CEC 2016), IEEE, Vancouver, Canada, 736--745.

[11]

A. Inselberg. 1985. The plane with parallel coordinates. The visual computer, 1,2 (1985), 69--91.

[12]

A. Lhuillier, C. Hurter, C. Jouffrais, E. Barbeau, HL Amieva. 2015. Visual analytics for the interpretation of fluency tests during Alzheimer evaluation. In Proceedings of the 2015 Workshop on Visual Analytics in Healthcare (VAHC '15). ACM, New York, NY, USA, Article 3, 8 pages.

Digital Library

[13]

M.H. Loorak, C. Perin, N. Kamal, M. Hill, S. Carpendale. 2016. TimeSpan: Using visualization to explore temporal multi-dimensional data of stroke patients. IEEE transactions on visualization and computer graphics, 22,1(2016), 409--418.

[14]

M. Petrovic, C. Hurt, D. Collins, A. Burns, V. Camus, R. Liperoti, A. Marriott, F. Nobili, P. Robert, M. Tsolaki, B. Vellas, F. Verhey, E.J. Byrne, Clustering of behavioural and psychological symptoms in dementia (BPSD): a European Alzheimer's disease consortium (EADC) study. Acta Clinica Belgica, 62, 6 (2008), 426--432.

[15]

C. Plaisant, R. Mushlin, A. Snyder, J. Li, D. Heller, & B. Shneiderman, (1998). LifeLines: using visualization to enhance navigation and analysis of patient records. In Proceedings of the AMIA Symposium (AMIA 1998). American Medical Informatics Association. Florida, United States, 76

[16]

M. Sedlmair., M. Meyer, & T. Munzner, Design study methodology: Reflections from the trenches and the stacks. IEEE Transactions on Visualization and Computer Graphics, 18,12(2012), 2431--2440.

Digital Library

[17]

JH Sung, DY Lee, HK Kim, Difference of GUI Efficiency based on 3D and 2D Graphic -Imaginary 3D IPTV Interface Development Using Virtual Reality Theory-, Journal of Content, 7,7(2007), 87--95.

[18]

P.N. Tan, M. Steinbach, V. Kumar, Introduction to Data Mining 1st Edition, 487--568, Addison-Wesley Longman Publishing Co., Inc. Boston, Chapter 8 Cluster Analysis: Basic Concepts and Algorithm

[19]

C.F. Wang, J. Li, K.L. Ma, C.W. Huang, Y.C. Li. 2014. A visual analysis approach to cohort study of electronic patient records. In Proceeding of the Bioinformatics and Biomedicine (BIBM 2014). IEEE, Belfast, UK, 521--528

[20]

H. Zhou, X. Yuan, H. Qu, W. Cui, B. Chen. 2008. Visual clustering in parallel coordinates. Computer Graphics Forum, 27, 3 (2008), 1047--1054.

Digital Library

Cited By

Ha HLee JHan HBae SSon SHong CShin HLee K(2019)Dementia Patient Segmentation Using EMR Data Visualization: A Design StudyInternational Journal of Environmental Research and Public Health10.3390/ijerph1618343816:18(3438)Online publication date: 16-Sep-2019
https://doi.org/10.3390/ijerph16183438

Index Terms

Visual analytics for biomedical cluster subdivision: a design study with psychiatrists
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User studies
  2. Visualization
    1. Visualization application domains
      1. Visual analytics

Recommendations

A visual analytics approach to understanding care process variation and conformance
VAHC '15: Proceedings of the 2015 Workshop on Visual Analytics in Healthcare

With greater pressures of providing high-quality care at lower cost due to a changing financial and policy environment, the ability to understand variations in care delivery and associated outcomes and act upon this understanding is of critical ...
Visual exploration of time-oriented patient data for chronic diseases: design study and evaluation
USAB'11: Proceedings of the 7th conference on Workgroup Human-Computer Interaction and Usability Engineering of the Austrian Computer Society: information Quality in e-Health

Medical care, particularly for chronic diseases, accumulates a huge amount of patient data over extensive time periods that needs to be accessed and analyzed accordingly. Information Visualization methods hold great promises in turning data deluge into ...
Improving Healthcare with Interactive Visualization

Visualization and visual analytics re-searchers can contribute substantial technological advances to support the reliable, effective, safe, and validated systems required for personal health, clinical healthcare, and public health policymaking. The Web ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

CGI '17: Proceedings of the Computer Graphics International Conference

June 2017

260 pages

ISBN:9781450352284

DOI:10.1145/3095140

Program Chairs:
Xiaoyang Mao
University of Yamanashi, Japan
,
Daniel Thalmann
NTU, Singapore & EPFL IC-DO, Switzerland
,
Marina Gavrilova
University of Calgary, Canada

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 June 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Funding Sources

Ministry of Health & Welfare, Republic of Korea
Ajou University
National Research Foundation of Korea

Conference

CGI '17

CGI '17: Computer Graphics International 2017

June 27 - 30, 2017

Yokohama, Japan

Acceptance Rates

Overall Acceptance Rate 35 of 159 submissions, 22%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
125
Total Downloads

Downloads (Last 12 months)8
Downloads (Last 6 weeks)0

Reflects downloads up to 24 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Ha HLee JHan HBae SSon SHong CShin HLee K(2019)Dementia Patient Segmentation Using EMR Data Visualization: A Design StudyInternational Journal of Environmental Research and Public Health10.3390/ijerph1618343816:18(3438)Online publication date: 16-Sep-2019
https://doi.org/10.3390/ijerph16183438

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents