# bernard2019dashboard

# Using dashboard networks to visualize multiple patient histories: A design study on post-operative prostate cancer


This paper presents a visualization technique that segments patient histories based on time and then aggregates the results by therapy states and biological conditions. Instead of treating patient histories as event sequences, the segmented results are presented using a static dashboard to show longitudinal changes.


The authors propose the technique for visualizing multivariate attribute sets, by using encoding variables with glyphs as shown in Table 1. A set of 15 variables are selected by domain experts who study prostate cancer. The segmentation of patient histories is achieved by using a sliding window approach with a step size of one week and a maximum window length of six months. The results are then aggregated by treatment combinations, therapy states and biological conditions, as shown in Figure 1.

Related Work

The authors review the approaches proposed in the survey of interactive visualization systems of EHR by Rind et al. [1].

The use of glyphs is inspired by Borgo et al. [2] and Loorak et al [3].

The authors adopt the technique of exploring of multivariate data cluster from the work of Cao et al [4].

Data Characteristics

  • Data source: unknown
  • Size: 2,000 patients
  • Spatial dimensionality: 2D
  • Temporal dimensionality: static
  • Type: multivariate, 15 variables

Visualization Techniques

  • Glyph
  • Candlestick chart

Papers Cited


Cites Glyph-based visualization: Foundations, design guidelines, techniques and applications.

Years Spanned


Application Domain

  • Information Visualization
  • Visual Analytics
  • Electronic Health Record Visualization
  • Medical focus: Oncology - Prostate Cancer


  1. Rind, A., Wang, T. D., Aigner, W., Miksch, S., Wongsuphasawat, K., Plaisant, C., & Shneiderman, B. (2011). Interactive Information Visualization to Explore and Query Electronic Health Records. Foundations and Trends® in Human–Computer Interaction, 5(3), 207–298. https://doi.org/10.1561/1100000039 ↩︎

  2. Borgo, R., Kehrer, J., Chung, D. H. S., Maguire, E., Laramee, R. S., Hauser, H., … Chen, M. (2013). Glyph-based Visualization: Foundations, Design Guidelines, Techniques and Applications. EG STAR. ↩︎

  3. Loorak, M. H., Perin, C., Collins, C., & Carpendale, S. (2017). Exploring the Possibilities of Embedding Heterogeneous Data Attributes in Familiar Visualizations. IEEE Transactions on Visualization and Computer Graphics. https://doi.org/10.1109/TVCG.2016.2598586 ↩︎

  4. Cao, N., Gotz, D., Sun, J., & Qu, H. (2011). DICON: Interactive visual analysis of multidimensional clusters. IEEE Transactions on Visualization and Computer Graphics. https://doi.org/10.1109/TVCG.2011.188 ↩︎

🔄 Last Updated: 7/8/2020, 9:48:18 PM