# loorak2016timespan

# TimeSpan: Using Visualization to Explore Temporal Multi-dimensional Data of Stroke Patients


TimeSpan is a visualization tool designed to explore the temporal aspects of stroke treatment process[1], as door-to-needle (DTN) time is the most important factor for treating stroke patients, TimeSpan focuses on correlating DTN with other data dimensions such as patient blood pressure and CT scan results etc. Based on the generated visualizations, stroke teams are able to change and improve the treatment process outside of clinical hours.

According to the investigation carried out by Loorak et al., stroke treatment process is highly team-based[1:1], a team often involves stroke neurologists for recognizing patients and performing the right treatment, quality assurance analysts for streamlining the treatment process and nurses for observing, collecting and presenting patient data as well as providing aftercare based on the data. This adds a challenge to develop TimeSpan, as choosing the right level of abstraction to preserve the information completeness is in conflict with the goal of improving the efficiency in stroke treatment process.


Based on 8 interviews with domain exports in stroke treatment, Loorak et al. combine an overview with a detailed view as shown in Figure 1, in order to satisfy the needs of all members of stroke treatment teams.

The overview section consists of a parallel coordinate for visualizing the selected temporal attributes for a cohort of patients, a text area for showing details of the selected patient and a panel for displaying sorting options.

The detailed view section consists of a query panel with user options and histograms with stacked bar charts and a matrix for visualizing additional attributes for the selected patients.

Related Work

Literature research is predominately based on the work of Rind et al.[2], which divides the existing EHR systems into two categories.

Systems focus on individual patients [3][4][5][6]. According to Loorak et al, those systems are not suitable for visualizing stroke patient data, the interviews with domain experts show that stroke patient data come with very different characteristics, especially in the temporal dimension.

Systems focus on a cohort of patients, Loorak et al. extensively review this category prior to the implementation of TimeSpan. Specify temporal queries with value and time span constraints[7], analyze disease progress and outcomes in patient records [8][9] and summarize and abstract key insights from patient records[10][11][12][13] are key areas reviewed by the authors.

Data Characteristics

  • Data source: Stroke Professionals
  • Size: 150 stroke patients
  • Spatial dimensionality: 2D
  • Temporal dimensionality: static
  • Type: multivariate

Visualization Techniques

  • Histogram
  • Parallel Coordinates
  • Stacked Bar Chart
  • Matrix

Papers Cited


Years Spanned


Application Domain

  • Electronic Health Record Visualization
  • Temporal Event Sequences
  • Medical focus: Neurology - Stroke


  1. Loorak, M. H., Perin, C., Kamal, N., Hill, M., & Carpendale, S. (2016). TimeSpan: Using Visualization to Explore Temporal Multi-dimensional Data of Stroke Patients. IEEE Transactions on Visualization and Computer Graphics. https://doi.org/10.1109/TVCG.2015.2467325 ↩︎ ↩︎

  2. 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 ↩︎

  3. Plaisant C, Milash B, Rose A, et al. LifeLines: visualizing personal histories. SIGCHI Conference on Human Factors in Computing Systems Proceedings; 1996:221–227. ↩︎

  4. Plaisant C, Mushlin R, Snyder A, et al. LifeLines: using visualization to enhance navigation and analysis of patient records. AMIA Symposium Proceedings; 1998:76–80. ↩︎

  5. Rind, A., Miksch, S., Aigner, W., Turic, T., & Pohl, M. (2010). VisuExplore: gaining new medical insights from visual exploration. Proceedings of the 1st International Workshop on Interactive Systems in Healthcare (WISH@CHI2010). ↩︎

  6. Faiola, A., & Newlon, C. (2011). Advancing critical care in the ICU: A human-centered biomedical data visualization systems. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/978-3-642-21716-6_13 ↩︎

  7. Fails, J. A., Karlson, A., Shahamat, L., & Shneiderman, B. (2006). A visual interface for multivariate temporal data: Finding patterns of events across multiple histories. IEEE Symposium on Visual Analytics Science and Technology 2006, VAST 2006 - Proceedings. https://doi.org/10.1109/VAST.2006.261421 ↩︎

  8. Wongsuphasawat K, Gotz D. Exploring flow, factors, and outcomes of temporal event sequences with the outflow visualization. IEEE Trans Vis Comput Graph. 2012;18: 2659–2668.34. ↩︎

  9. Gotz, D., & Stavropoulos, H. (2014). DecisionFlow: Visual analytics for high-dimensional temporal event sequence data. IEEE Transactions on Visualization and Computer Graphics. https://doi.org/10.1109/TVCG.2014.2346682 ↩︎

  10. Klimov D, Shahar Y, Taieb-Maimon M. Intelligent visualization and exploration of time-oriented data of multiple patients. ArtifIntellMed. 2010;49:11–31. ↩︎

  11. Shahar Y, Cheng C. Model-based visualization of temporal abstractions. ComputIntell. 2000;16:279–306. ↩︎

  12. Martins SB, Shahar Y, Goren-Bar D, et al. Evaluation of an architecture for intelligent query and exploration of time-oriented clinical data. ArtifIntellMed. 2008;43:17–34. ↩︎

  13. 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. ↩︎

🔄 Last Updated: 7/8/2020, 9:48:18 PM