Optimizing Symptom Surveys with Machine Learning to Predict PTSD One Year Post-trauma

Post-traumatic stress disorder (PTSD), a lasting mental health disorder, can be a burden to monitor for patients and clinicians. This work introduces AI-driven methods to understand the predictive value of behavioral symptom surveys for PTSD diagnosis one year post-trauma. We find that surveys issued at regular time intervals can predict PTSD diagnosis with moderate accuracy of around 75% (AUC ~ 0.83). Then, using machine learning algorithms for feature selection, we develop a survey minimization method in which a patient is only required to answer up to seven questions at each time point. Finally, we introduce a tree-based algorithm for individualized, adaptive surveys that approximate PTSD checklist (PCL-5) scores with high precision, allowing for prediction of diagnoses with comparable accuracy to the full survey set. These findings offer resource-efficient AI interventions for PTSD monitoring that are readily deployable in a clinical setting.