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Multivariate time series (MTS) have become increasingly common in healthcaredomains where human vital signs and laboratory results are collected forpredictive diagnosis. Recently, there have been increasing efforts to visualizehealthcare MTS data based on star charts or parallel coordinates. However, suchtechniques might not be ideal for visualizing a large MTS dataset, since it isdifficult to obtain insights or interpretations due to the inherent highdimensionality of MTS. In this paper, we propose 'm-TSNE': a simple and novelframework to visualize high-dimensional MTS data by projecting them into alow-dimensional (2-D or 3-D) space while capturing the underlying dataproperties. Our framework is easy to use and provides interpretable insightsfor healthcare professionals to understand MTS data. We evaluate ourvisualization framework on two real-world datasets and demonstrate that theresults of our m-TSNE show patterns that are easy to understand while the othermethods' visualization may have limitations in interpretability.

m-TSNE: A Framework for Visualizing High-Dimensional Multivariate Time Series