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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.