@Article{info:doi/10.2196/13946,
author="Wshah, Safwan
and Skalka, Christian
and Price, Matthew",
title="Predicting Posttraumatic Stress Disorder Risk: A Machine Learning Approach",
journal="JMIR Ment Health",
year="2019",
month="Jul",
day="22",
volume="6",
number="7",
pages="e13946",
keywords="PTSD; machine learning; predictive algorithms",
abstract="Background: A majority of adults in the United States are exposed to a potentially traumatic event but only a handful go on to develop impairing mental health conditions such as posttraumatic stress disorder (PTSD). Objective: Identifying those at elevated risk shortly after trauma exposure is a clinical challenge. The aim of this study was to develop computational methods to more effectively identify at-risk patients and, thereby, support better early interventions. Methods: We proposed machine learning (ML) induction of models to automatically predict elevated PTSD symptoms in patients 1 month after a trauma, using self-reported symptoms from data collected via smartphones. Results: We show that an ensemble model accurately predicts elevated PTSD symptoms, with an area under the curve (AUC) of .85, using a bag of support vector machines, naive Bayes, logistic regression, and random forest algorithms. Furthermore, we show that only 7 self-reported items (features) are needed to obtain this AUC. Most importantly, we show that accurate predictions can be made 10 to 20 days posttrauma. Conclusions: These results suggest that simple smartphone-based patient surveys, coupled with automated analysis using ML-trained models, can identify those at risk for developing elevated PTSD symptoms and thus target them for early intervention. ",
issn="2368-7959",
doi="10.2196/13946",
url="http://mental.jmir.org/2019/7/e13946/",
url="https://doi.org/10.2196/13946",
url="http://www.ncbi.nlm.nih.gov/pubmed/31333201"
}