Using Convolutional Variational Autoencoders to Predict Post-Trauma Health Outcomes from Actigraphy Data

• URL: http://arxiv.org/abs/2011.07406v2
• Date: Fri, 20 Nov 2020 02:52:44 GMT
• Title: Using Convolutional Variational Autoencoders to Predict Post-Trauma Health Outcomes from Actigraphy Data
• Authors: Ayse S. Cakmak, Nina Thigpen, Garrett Honke, Erick Perez Alday, Ali Bahrami Rad, Rebecca Adaimi, Chia Jung Chang, Qiao Li, Pramod Gupta, Thomas Neylan, Samuel A. McLean, Gari D. Clifford
• Abstract summary: Depression and post-traumatic stress disorder (PTSD) are psychiatric conditions commonly associated with a traumatic event. In this work, we used locomotor activity captured from 1113 individuals who wore a research grade smartwatch post-trauma. A convolutional variational autoencoder (VAE) architecture was used for unsupervised feature extraction from actigraphy data.
• Score: 4.668948267866486
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Depression and post-traumatic stress disorder (PTSD) are psychiatric conditions commonly associated with experiencing a traumatic event. Estimating mental health status through non-invasive techniques such as activity-based algorithms can help to identify successful early interventions. In this work, we used locomotor activity captured from 1113 individuals who wore a research grade smartwatch post-trauma. A convolutional variational autoencoder (VAE) architecture was used for unsupervised feature extraction from four weeks of actigraphy data. By using VAE latent variables and the participant's pre-trauma physical health status as features, a logistic regression classifier achieved an area under the receiver operating characteristic curve (AUC) of 0.64 to estimate mental health outcomes. The results indicate that the VAE model is a promising approach for actigraphy data analysis for mental health outcomes in long-term studies.

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