DecisionFlow: Temporal Pattern Analysis and Visualization
Patients’ medical conditions often evolve in complex and seemingly unpredictable ways. Even within a relatively narrow and well-defined episode of care, variations between patients in both their progression and eventual outcome can be dramatic. Understanding the patterns of events observed within a population that most correlate with differences in outcome is therefore an important task in many types of studies using retrospective electronic health data. In this paper, we present a method for interactive pattern mining and analysis that supports ad hoc visual exploration of patterns mined from retrospective clinical patient data. Our approach combines (1) visual query capabilities to interactively specify episode definitions, (2) pattern mining techniques to help discover important intermediate events within an episode, and (3) interactive visualization techniques that help uncover event patterns that most impact outcome and how those associations change over time. In addition to presenting our methodology, we describe a prototype implementation and present use cases highlighting the types of insights or hypotheses that our approach can help uncover.
David Gotz and Harry Stavropoulos. DecisionFlow: Visual Analytics for High-Dimensional Temporal Event Sequence Data. IEEE Transactions on Visualization and Computer Graphics (2014).
David Gotz and Jimeng Sun. Visualizing Accuracy to Improve Predictive Model Performance. IEEE VIS Workshop on Visualizing Predictive Anlaytics (2014).
David Gotz, Fei Wang, and Adam Perer. A Methodology for Interactive Mining and Visual Analysis of Clinical Event Patterns Using Electronic Health Record Data. Journal of Biomedical Informatics (2014).