Artificial Intelligence Advancements for Personalized PTSD Research

Abstract

Post-traumatic stress disorder (PTSD) research grapples with numerous challenges, including the wide variability in symptoms among individuals, the complexity of comorbidities, and the scarcity of predictive biomarkers. Integrating diverse data sources and translating research findings into clinical practice further compounds these challenges. These challenges prompted the exploration of machine learning, deep learning, and natural language processing techniques to enhance disorder classification, outcome prediction, and personalized treatment selection. A systematic review of 69 studies, drawn from a pool of 364 abstracts identified through PubMed, Embase, and Web of Science, revealed the diverse applications of machine learning, deep learning, and natural language processing in this domain. Studies predominantly utilized multiple data types to predict risk factors or early symptoms related to PTSD, while other artificial intelligence (AI) techniques aimed to differentiate symptoms of PTSD from those with other psychiatric disorders or controls. The findings highlight the suitability of artificial intelligence for addressing the heterogeneity of ASD/PTSD patients, with the future challenge lying in translating these advancements into practical clinical applications for individualized patient benefits.