Visually defining and querying consistent multi-granular clinical temporal abstractions
Artificial Intelligence in Medicine
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The human vision can naturally interpret data in spaces of 2 or 3 dimensions. When data is in higher dimensional spaces, in most cases the visualization is not intuitive. Regarding metric spaces, the interpretation is even harder, since they often do not have a direct spatial representation. However, the need to analyze how metric-represented data evolve over time is pretty common when one needs to understand several phenomena and in decision making processes, as it occurs in medical and agrometeorological applications. This paper presents three interactive techniques to visualize metric data that vary over time. Each one focus on a different way to interpret the temporal information. The first technique shows data evolving in a timeline axis. The second overlaps evolving snapshots of the space showing how the space varies regarding time. The last one does not treat temporal data as a dimension, it is used instead to define the similarity among complex data, employing the new concept of metric-temporal spaces, which seamlessly integrate time and metric data into a single similarity space. Visualization examples with real datasets are presented to show the usefulness of the proposed techniques.