# onukwugha2016cancer

# Data Visualization Tools for Investigating Health Services Utilization Among Cancer Patients

Concept

This paper reviews the use of existing information visualization systems for exploring and querying EHRs in oncology, by evaluating their techniques, designs and effectiveness in generating insightful and actionable evidence using targeted case studies [1].

Classification

There is no clear classification available in the survey, nor a classification table.

The authors identify common techniques used in existing implementations.

  1. The use of Geographic Information Systems (GIS) to visualize data on maps.
  2. The use of colors and glyphs to represent data dimensions.
  3. Data stream clustering, which is used to reduce the dimensionality and noise in high frequency data streams.
  4. The use of directed graphs to represent the relationships between events.
  5. The use of hierarchical visualizations to provide detailed information on different hierarchical levels.
  6. The use of neural networks to project a high dimensional and time-varying dataset.

The authors describe EventFlow [2] and Cohort Comparison (CoCo) [3] in detail with case studies visualizing a prostate cancer dataset and a health insurance claim dataset.

They also review 22 other EHR systems very briefly.

Challenges

The authors identify three major challenges:

  1. Develop algorithms for processing massive, but imperfect data.
  2. Create effective human–computer interaction tools for visual reasoning.
  3. Distill millions of EHRs into cogent visualizations to guide decision makers.

Unsolved Problems

  1. Systematic errors in data are difficult to identify and rectify.
  2. Efficient mechanisms to explore relationships in a big data context is missing.

Papers Cited

62

Years Spanned

1998-2015

Application Domain

Information Visualization of EHR.

Reference

  1. Onukwugha, E., Plaisant, C., & Shneiderman, B. (2016). Data Visualization Tools for Investigating Health Services Utilization Among Cancer Patients. In Oncology Informatics. https://doi.org/10.1016/b978-0-12-802115-6.00011-2 ↩︎

  2. M. Monroe, R. Lan, H. Lee, C. Plaisant, and B. Shneiderman. Temporal event sequence simplification. IEEE transactions on visualization and computer graphics, 19(12):2227–2236, 2013. ↩︎

  3. Malik, S., Du, F., Monroe, M., Onukwugha, E., Plaisant, C., & Shneiderman, B. (2015). Cohort Comparison of Event Sequences with Balanced Integration of Visual Analytics and Statistics. Proceedings of the 20th International Conference on Intelligent User Interfaces - IUI ’15. https://doi.org/10.1145/2678025.2701407 ↩︎

🔄 Last Updated: 7/8/2020, 9:48:18 PM

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