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We conducted a systematic review and random-effect meta-analysis summarizing predictive model development and validation studies using machine learning in diverse samples to predict PTSD. Model performances were pooled using the area under the curve (AUC) with a 95% confidence interval (CI). Heterogeneity in each meta-analysis was measured using I<jats:sup>2<\/jats:sup>. The risk of bias in each study was appraised using the PROBAST tool. 48% of the 23 included studies had a high ROB, and the remaining had unclear. Tree-based models were the primarily used algorithms and showed promising results in predicting PTSD outcomes for various groups, as indicated by their pooled AUCs: military incidents (0.745), sexual or physical trauma (0.861), natural disasters (0.771), medical trauma (0.808), firefighters (0.96), and alcohol-related stress (0.935). However, the applicability of these findings is limited due to several factors, such as significant variability among the studies, high and unclear risks of bias, and a shortage of models that maintain accuracy when tested in new settings. Researchers should follow the reporting standards for AI\/ML and adhere to the PROBAST guidelines. It is also essential to conduct external validations of these models to ensure they are practical and relevant in real-world settings.<\/jats:p>","DOI":"10.1186\/s12911-024-02754-2","type":"journal-article","created":{"date-parts":[[2025,1,21]],"date-time":"2025-01-21T12:55:29Z","timestamp":1737464129000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis"],"prefix":"10.1186","volume":"25","author":[{"given":"Masoumeh","family":"Vali","sequence":"first","affiliation":[]},{"given":"Hossein Motahari","family":"Nezhad","sequence":"additional","affiliation":[]},{"given":"Levente","family":"Kovacs","sequence":"additional","affiliation":[]},{"given":"Amir H","family":"Gandomi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,21]]},"reference":[{"key":"2754_CR1","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1002\/jts.21848","volume":"26","author":"DG Kilpatrick","year":"2013","unstructured":"Kilpatrick DG, Resnick HS, Milanak ME, Miller MW, Keyes KM, Friedman MJ. 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