UMBC High Performance Computing Facility
Please note that this page is under construction. We are documenting the
240-node cluster maya that will be available after Summer 2014.
Currently, the 84-node cluster tara still operates independently,
until it becomes part of maya at the end of Summer 2014.
Please see the 2013 Resources Pages under the Resources tab for tara information.
Multivariate Time-Series Analysis of Physiological
and Clinical Data
Marie desJardins, CSEE, UMBC
Tim Oates, CSEE, UMBC
Patricia Ordonez Rozo, CSEE, UMBC
Jim Fackler, JHU
Chris Lehmann, JHU
Although the sophistication and volume of collected data is greater now
than at any point in the history of medicine, the information overload
that providers face may inhibit the diagnostic process (Heldt et al.
2006). Medical providers are expected to examine the large volume of
data and identify correlations among parameters based on their own
clinical experience to detect significant events or conditions. Most
existing visualizations of the data to assist the provider in analyzing
the information consist of a table or plot of values for a particular
parameter as a function of time. Automated techniques for discovering
these correlations not only may assist the provider in making a
diagnosis but may help to identify hidden patterns within the data
associated with specific medical conditions or events. Current
visualization and machine learning techniques show promise for
extracting this information.
This dissertation presents three novel representations and two
visualizations to assist in the analysis of multivariate time series
data. It focuses on physiological and clinical data, in particular,
because this type of data captures the complexity of a human being, and
thus, the multivariate time series in this type of data are more
interdependent and synchronized than most. The three representations are
the Multivariate Time Series Amalgam (MTSA), the Stacked
Bags-of-Patterns (Stacked BoP), the Multivariate Bag-of-Patterns
(Multivariate BoP). Each provides an integrated, multivariate approach
for representing multivariate time series data.
The MTSA representation is the foundation for two visualizations - the
MTSA Visualization and the Fixed Dual Visualization. These animated
visualizations capture the rate of change of provider-selected
parameters and the relationships among them. While both visualizations
were created for the medical domain, they generalize to domains where
multiple variables measure the state of an entity as a function of time.
An evaluation of the Fixed Dual Visualization was carried out with 23
pediatric residents at Johns Hopkins University School of Medicine. The
results indicate that the visualization merits further investigation for
use as a diagnostic tool.