m-TSNE: A Framework for Visualizing High-Dimensional Multivariate Time Series
This addresses the challenge for healthcare professionals in interpreting large MTS datasets, though it appears incremental as it builds on existing visualization techniques.
The paper tackled the problem of visualizing high-dimensional multivariate time series (MTS) in healthcare by proposing m-TSNE, a framework that projects MTS into low-dimensional spaces, resulting in patterns that are easier to understand compared to other methods.
Multivariate time series (MTS) have become increasingly common in healthcare domains where human vital signs and laboratory results are collected for predictive diagnosis. Recently, there have been increasing efforts to visualize healthcare MTS data based on star charts or parallel coordinates. However, such techniques might not be ideal for visualizing a large MTS dataset, since it is difficult to obtain insights or interpretations due to the inherent high dimensionality of MTS. In this paper, we propose 'm-TSNE': a simple and novel framework to visualize high-dimensional MTS data by projecting them into a low-dimensional (2-D or 3-D) space while capturing the underlying data properties. Our framework is easy to use and provides interpretable insights for healthcare professionals to understand MTS data. We evaluate our visualization framework on two real-world datasets and demonstrate that the results of our m-TSNE show patterns that are easy to understand while the other methods' visualization may have limitations in interpretability.