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

TL;DR

m-TSNE is a new visualization framework that effectively projects high-dimensional multivariate time series data into low-dimensional space, aiding healthcare professionals in understanding complex data patterns with improved interpretability.

Contribution

This paper introduces m-TSNE, a novel and simple framework for visualizing high-dimensional MTS data in low-dimensional space, enhancing interpretability for healthcare applications.

Findings

m-TSNE reveals clear data patterns in real-world healthcare datasets.

Compared to existing methods, m-TSNE offers better interpretability.

Visualizations from m-TSNE are easier for professionals to understand.

Abstract

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.