UBIQUITOUS PHYSIOLOGICAL PREDICTION OF SUD PATIENTS? WELLNESS STATE USING MEMORY-BASED CONVOLUTIONAL MODELS

The prevalence of substance use disorder (SUD) and rates of overdose in the United States have reached epidemic levels. Despite availability of effective evidence-based treatments for SUD, the rates of treatment attrition remain elevated. We have designed a cloud-based continuous physiological sensing for longitudinal SUD patient monitoring. Using wearable sensors, we aim to evaluate the impact of changes in heart rate (HR) and heart rate variability (HRV) signals on SUD wellness development using long-term and ubiquitous monitoring and machine learning and collected data from 10 subjects over an extended period of time. We designed a signal processing recipe and employed several recurrent neural network (RNN)-based architectures to track the temporal and spectral behavior of HR and HRV signals to predict the patients? wellness state. In addition, we have designed an architecture that combines RNN architectures and Time Scattered convolutional neural networks (TS-CNNs), where CNNs objectify the underlying features in the temporal dimensions within HR signals (TS). The goal is to quantitatively and qualitatively evaluate the contribution of TS-CNNs in ubiquitous wellness prediction. The experimental results demonstrate that the best architecture configuration achieves 90.21% accuracy in predicting the wellness state of the SUD patients.