# zhang2019temporal

# IDMVis: Temporal Event Sequence Visualization for Type 1 Diabetes Treatment Decision Support


Zhang et al. introduce IDMVis [1], an interactive visualization tool for visualizing type 1 diabetes data for a single patient. IDMVis includes a novel technique for folding and aligning data and scaling the intermediate timeline.

The authors design IDMVis based on a qualitative evaluation carried out with six domain experts in diabetes care.


Figure 1 shows an overview of IDMVis. Section A shows a 14-day overview of the patient's glucose level. In the trend lines, green indicates a normal glucose level, purple indicates a high glucose level and orange indicates a low glucose level. Each blue triangle represents an event logged in the patient's logbook. Section B shows a detail panel for the selected day. Section C shows the distribution of insulin and carbohydrate intake for the selected event.

Figure 2 shows the single-event alignment feature, it allows the user to specify an event and align the trend lines accordingly. Figure 3 shows the dual-event alignment feature, which aligns the tread lines between two selected events.

Related Work

The authors build IDMVis on top of exisiting EHR visualization systems from the survey by Rind et al. [2] and a book chapter by Rind et al. [3]. Temporal folding of events is inspired by LifeLine2 [4] and CareCruiser[5].

IDMVis is focusing on the visualization of single patient data, the authors review early relevant researches such as the visual display of temporal information [6], KNAVE[7] and KNAVE II[8].

Data Characteristics

Visualization Techniques

  • Glyph
  • Line chart

Papers Cited


Years Spanned


Application Domain

  • Electronic Health Record Visualization
  • Temporal Event Sequence
  • Medical focus: Endocrinology - Type 1 Diabetes


  1. Zhang, Y., Chanana, K., & Dunne, C. (2019). IDMVis: Temporal Event Sequence Visualization for Type 1 Diabetes Treatment Decision Support. IEEE Transactions on Visualization and Computer Graphics, 25(1), 512–522. https://doi.org/10.1109/TVCG.2018.2865076 ↩︎

  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. Rind, A., Federico, P., Gschwandtner, T., Aigner, W., Doppler, J., & Wagner, M. (2017). Visual Analytics of Electronic Health Records with a Focus on Time. In F. Capello, G. Rinaldi, & G. Gatti (Eds.), New Perspectives in Medical Records (pp. 65–77). https://doi.org/10.1007/978-3-319-28661-7 ↩︎

  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. Ozturk, S., Kayaalp, M., & McDonald, C. J. (2014). Visualization of patient prescription history data in emergency care. AMIA Annual Symposium Proceedings. AMIA Symposium. ↩︎

  6. Cousins, S. B., & Kahn, M. G. (1991). The visual display of temporal information. Artificial Intelligence In Medicine. https://doi.org/10.1016/0933-3657(91)90005-V ↩︎

  7. Shahar, Y., & Cheng, C. (2000). Model-based visualization of temporal abstractions. Computational Intelligence. https://doi.org/10.1111/0824-7935.00114 ↩︎

  8. Martins, S. B., Shahar, Y., Goren-Bar, D., Galperin, M., Kaizer, H., Basso, L. V., … Goldstein, M. K. (2008). Evaluation of an architecture for intelligent query and exploration of time-oriented clinical data. Artificial Intelligence in Medicine. https://doi.org/10.1016/j.artmed.2008.03.006 ↩︎

🔄 Last Updated: 7/8/2020, 9:48:18 PM