We present the design and evaluation of an integrated problem solving environment for cancer therapy analysis. The environment intertwines a statistical martingale model and a K Nearest Neighbor approach with visual encodings, including novel interactive nomograms, in order to compute and explain a patient's probability of survival as a function of similar patient results. A coordinated views paradigm enables exploration of the multivariate, heterogeneous and few-valued data from a large head and neck cancer repository. A visual scaffolding approach further enables users to build from familiar representations to unfamiliar ones. Evaluation with domain experts show how this visualization approach and set of streamlined workflows enable the systematic and precise analysis of a patient prognosis in the context of cohorts of similar patients. We describe the design lessons learned from this successful, multi-site remote collaboration.
|Original language||English (US)|
|Number of pages||14|
|Journal||IEEE Transactions on Visualization and Computer Graphics|
|State||Published - Apr 1 2019|
Bibliographical noteFunding Information:
This work was supported by the National Institutes of Health [NCI-R01-CA214825, NCI-R01CA225190]; and the US National Science Foundation [NSF-CNS-1625941, NSF-DMS-1557559]. The authors thank all members of the Electronic Visualization Laboratory, all members of the MD Anderson Head and Neck Cancer Quantitative Imaging Collaborative Group, and in particular Hesham Elhalawani and Tommy Sheu.
- Visual analytics
- activity-centered design
- design studies
- parallel coordinate plots
- precision medicine