Innovations in Visualization

TimeSpan: Using Visualization to Explore Temporal Multidimensional Data of Stroke Patients

Mona Hosseinkhani
Charles Perin
Noreen Kamal
Michael Hill
Sheelagh Carpendale



TimeSpan is a visualization tool designed to gain a better understanding of the temporal aspects of the stroke treatment process. Working with clinical stroke experts, we seek to improve outcomes for stroke victims. Time is of critical importance in the treatment of acute ischemic stroke patients. Every minute that the artery stays blocked, an estimated 1.9 million neurons and 12 km of myelinated axons are destroyed. Consequently, there is a critical need for efficiency of stroke treatment processes. Optimizing time to treatment requires a deep understanding of interval times. Stroke health care professionals must analyze the impact of procedures, events, and patient attributes on time---ultimately, to save lives and improve quality of life after stroke. First, we interviewed eight domain experts, and closely collaborated with two of them to inform the design of TimeSpan. In this paper, we report on the tasks and system requirements that we extracted and classified in order to assess the tasks which a visualization tool could support. Based on these tasks and the understanding gained from the collaboration, we designed TimeSpan, a web-based tool for exploring multidimensional and temporal stroke data. We describe how TimeSpan incorporates factors from stacked bar graphs, line charts, histograms, and a matrix visualization to create an interactive hybrid view of temporal data. From feedback collected from domain experts in a focus group session, we reflect on the lessons we learned from abstracting the tasks and iteratively designing this visualization system.

Supplemental materials are available here.


Download video

Download Full Video


Mona Hosseinkhani Loorak, Charles Perin, Noreen Kamal, Michael Hill, and Sheelagh Carpendale. TimeSpan: Using Visualization to Explore Temporal Multi-dimensional Data of Stroke Patients. IEEE Transactions on Visualization and Computer Graphics, 22(1):409-418, 2016. PDF Paper