Machine Learning

Prediction of Fluctuations in Depression and PTSD symptoms in World Trade Center Responders: A Longitudinal Sensing and Ecological Momentary Assessment Study
Ecological momentary assessment (EMA) and passive sensing data from a large sample of World Trade Center (WTC) first responders experincing depression and PTSD symptoms will be used to explore the utility of traditional machine learning and deep learning models in prediction of real-time symptom change.
Prediction of Fluctuations in Depression and PTSD symptoms in World Trade Center Responders: A Longitudinal Sensing and Ecological Momentary Assessment Study
Using Smartphone Sensing Data and Machine Learning to Predict Trajectories of COVID-19-Related Life Stress
The COVID-19 pandemic disrupted the daily lives of individuals worldwide, contributing to significant increases in life stress. Individuals with mental health disorders are known to be at risk for worsening of symptoms and functioning during periods of increased stress, making vulnerable during the COVID-19 pandemic.
Using Smartphone Sensing Data and Machine Learning to Predict Trajectories of COVID-19-Related Life Stress