HiddenVis: a Hidden State Visualization Toolkit to Visualize and Interpret Deep Learning Models for Time Series Data

Abstract

Accelerometers provide continuous measurements of human movements. Utilizing their data exhibits great potential for monitoring or predicting human health outcomes. In this direction, researchers are active in developing machine-learning algorithms. However, compared to understanding deep learning models, visualizing and interpreting such models is equally important but has been less well-developed. To address these limitations, we constructed an online software tool to visualize the hidden states inferred from the deep learning models and to compare different cohorts based on outcome-associated temporal profiles. We demonstrated the utility of the model using the National Health and Nutrition Examination Survey (NHANES) dataset. Using this tool, we discovered that two time periods of daily activity contribute to significant differences in 5-year mortalities. To disseminate the software to the broader community for the analysis of accelerometer data, we provide the work as open-source code at https://github.com/jyan97/HiddenVis.