Predicting PTSD Severity in Veterans from Self-reports for Early Intervention: A Machine Learning Approach

Early intervention for veterans in crisis represents a crucial area of study to reduce the psychological and health burdens for this population. Traumatic experiences associated with military service are associated with drug and alcohol abuse, suicidality, anger, and disrupted work and family relationships. This project used machine learning (ML) models to integrate data from sociodemographic, self-report baseline symptoms, weekly brief Ecological momentary assessment (EMA) survey of veterans in a community-based 12-week peer support program to predict the discharge PTSD severity level. The ML predictions place the participants into one of the three risk levels: low, medium, and high PCL-5 score. The models were evaluated at different timepoints (weekly intervals) of the program for identifying the earliest week to guide early intervention and reduce veterans' engagement in risky behaviors. The best results were achieved from a voting classifier with an average f-score of 0.69 at week 4.