TY  - JOUR
AU  - Wshah, Safwan
AU  - Skalka, Christian
AU  - Price, Matthew
PY  - 2019
DA  - 2019/07/22
TI  - Predicting Posttraumatic Stress Disorder Risk: A Machine Learning Approach
JO  - JMIR Ment Health
SP  - e13946
VL  - 6
IS  - 7
KW  - PTSD
KW  - machine learning
KW  - predictive algorithms
AB  - 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. 
SN  - 2368-7959
UR  - http://mental.jmir.org/2019/7/e13946/
UR  - https://doi.org/10.2196/13946
UR  - http://www.ncbi.nlm.nih.gov/pubmed/31333201
DO  - 10.2196/13946
ID  - info:doi/10.2196/13946
ER  -