CONTACT US: Contact info
|
Citation |
McDonald AD, Sasangohar F, Jatav A, Rao AH. IISE Transactions on Healthcare Systems Engineering 2019; 9(3): 201-211. |
|
Copyright |
(Copyright © 2019) |
|
DOI |
|
PMID |
unavailable |
|
Abstract |
Post-traumatic stress disorder (PTSD) is a prevalent mental health condition among United States combat veterans, associated with high incidence of suicide and substance abuse. While PTSD treatments exist, such methods are limited to in-person therapy sessions and medications. Tools and technologies to monitor patients continuously, especially between sessions, are largely absent. This article documents efforts to develop predictive algorithms that utilize real-time heart rate data, collected using commercial off-the-shelf wearable sensors, to detect the onset of PTSD triggers. The heart rate data, pre-processed with a Kalman filter imputation approach to resolve missing data, were used to train five algorithms: decision tree, support vector machine, random forest, neural network, and convolutional neural network. Prediction performance was assessed with the Area Under the receiver operating characteristic Curve (AUC). The convolutional neural network, support vector machine, and random forests had the highest AUC and significantly outperformed a random classifier. Further analysis of the heart rate data and predictions suggest that the algorithms associate an increase in heart rate with PTSD trigger onset. While work is needed to enhance algorithm performance and robustness, these results suggest that wearable monitoring technology for PTSD symptom mitigation is an achievable goal in the near future. © 2019, © 2019 "IISE". Language: en |
|
Keywords |
United States; adult; human; suicide; aged; incidence; technology; war; post-traumatic stress disorder; veteran; monitoring; posttraumatic stress disorder; substance abuse; major clinical study; algorithm; heart rate; Article; decision tree; support vector machine; random forest; classifier; convolutional neural network; Convolutional neural networks; human physiology; supervised machine learning; wearable technology |