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Nature Mental Health (2025)
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In recent years, the application of machine learning (ML) techniques in research on the prediction of post-traumatic stress disorder (PTSD) has increased. However, concerns regarding the clinical relevance and generalizability of ML findings hamper their implementation by clinicians and researchers. Here in this systematic review we examined (1) the extent to which pre-, peri- and post-traumatic risk factors identified using ML approaches coincide with the theoretical understanding of the disorder; (2) whether new insights were gained through ML techniques; and (3) whether ML findings, combined with previous research, enable an integrative model of PTSD risk encompassing both predictor categories and their theoretical relevance. We reviewed ML studies on PTSD risk factors in PubMed, Web of Science and Scopus. Studies were included if they specified when predictors and PTSD symptoms were collected in temporal relation to the traumatic event. A total of 30 studies with 12,908 participants (mean age 36.5 years) were included. After extracting the 15 most important predictors from all studies, we categorized them into pre-, peri- and post-trauma exposure predictors and examined their associations with established theoretical models of PTSD. Many studies exhibited a risk of bias, assessed using the prediction model risk of bias assessment tool (PROBAST). However, we found overlaps in identified predictors across studies, a concordance between data-driven results and theory-driven research, and underexplored predictors identified through ML. We propose an integrative model of PTSD risk that incorporates both data-driven and theory-driven findings and discuss future directions. We emphasize the importance of standards on how to apply and report ML approaches for mental health.
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K.S. received support from the National Institute of Mental Health (R01MH129856) and the National Heart, Lung, and Blood Institute (R01HL156134). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Univ. Lille, Inserm, CHU Lille, U1172-LilNCog-Lille Neuroscience & Cognition, Lille, France
Wivine Blekic & Fabien D’Hondt
Centre national de ressources et de résilience Lille-Paris, Lille, France
Fabien D’Hondt
Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, USA
Arieh Y. Shalev & Katharina Schultebraucks
Department of Population Health, NYU Grossman School of Medicine, New York, NY, USA
Katharina Schultebraucks
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Conceptualization: W.B. and K.S. Consensus on the string keywords: W.B. and K.S. Database searches, article selection and extraction of information: W.B. Discussions on paper retention: W.B. and K.S. Draft writing: W.B. and K.S. Draft editing: W.B., K.S., A.Y.S. and F.D. Manuscript revision: W.B., K.S., A.Y.S. and F.D. All authors reviewed and approved the final manuscript.
Correspondence to Katharina Schultebraucks.
The authors declare no competing interests.
Nature Mental Health thanks Jacklynn Fitzgerald, Gosia Lipinska and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Flow chart describing inclusion and exclusion of reviewed studies.
Extended data references list.
All 76 models are presented in detail in ‘Included studies’.
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Blekic, W., D’Hondt, F., Shalev, A.Y. et al. A systematic review of machine learning findings in PTSD and their relationships with theoretical models. Nat. Mental Health (2025). https://doi.org/10.1038/s44220-024-00365-4
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