Computational Approaches to PTSD in the Middle East

Abstract

Posttraumatic stress disorder (PTSD) poses a major mental health threat in the Middle East, where people experience ongoing conflict, displacement, and trauma. This study summarizes the use of computational and data-driven approaches, including machine learning (ML), biomarker identification, network analysis, and factor analysis, to understand, diagnose, and treat PTSD in the Middle East. A detailed search of the Scopus database led to the inclusion of only relevant original/research studies that met strict inclusion criteria for human subjects, geographical focus, and use of computational approaches. Studies have suggested that computational models can predict PTSD onset, biomarkers from biology and neuroimaging, complex symptoms, and comorbidity networks. They enable the development of precision psychiatry and culturally adapted diagnostic measures for conflict-affected displaced population samples. The relevance of multimodal pre-trauma screening, as well as the importance of consistent biological markers (inflammation, gene expression), is underscored by a comparative analysis of predictive models (AUCs of 0.73-0.92). Nonetheless, the cross-cultural validity of PTSD symptoms and the presence of computer resources in the region remain concerning. This article describes the revolutionary potential of computational psychiatry for improving mental health research and practice in the Middle East. It emphasizes the importance of culture-sensitive, scalable application with respect to resources.