Publications in Scientific Journals:

C. Urach, H. Piringer, N. Popper, F. Breitenecker:
"Parallel sets as interfactive visualization approach ior analyzing markov models";
Value In Health, 15 (2012), 7; 473 - 474.

English abstract:
OBJECTIVES: Commonmethods to visualize Markov states over time (e.g., Markovcycle
trees or state probability graphs) do not scale well to many cycles and have
limitations concerning the perception of proportions. An objective of the IFEDH
research project (FFG grant number 827347) was to overcome these limits by investigating
new visualization methods of Markov models and their results. METHODS:
Inspired by the "Parallel Coordinates", an interactive technique called Parallel Sets
has been developed for visualizing multidimensional categorical data. The visualization
lays out axes in a parallel way where each axis represents one categorical
dimension. Within each axis, boxes represent the categories which are scaled according
to the respective frequencies. Applied to Markov Models, the categorical
dimensions correspond to the various cycles. Joint probabilities of categories from
adjacent axes are shown as parallelograms connecting the respective categories.
The parallelograms can be interpreted as the number of patients transiting from one state to another. Depending on the purpose, the color of the parallelograms
indicates the categories of a chosen cycle or could refer to additional attributes of
the patients like age or sex. RESULTS: State probability and survival curves merely
show specific aggregates of the data while classic Markov trace visualizations with
for example bubble diagrams do not visualize data in a sense that would facilitate
a detection of proportions and trends. Applying Parallel Sets to analyze Markov
models provides an interactive visualization technique where changing the reference
Markov cycle is as easy as highlighting particular dimensions, thus enabling
the exploration of the progress of patient cohorts with certain characteristics
through the model. CONCLUSIONS: Model development always requires thorough
analysis of its structure, behavior and results. Parallel Sets enable an intuitive and
efficient visualization technique for presentation purposes as well as exploratory

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