Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis

What the review found when it combined the studies

These are the review’s own pooled results, copied from its abstract. Truvace did not recalculate them.

• PTSD prediction by tree-based models in people with alcohol-related stress

  AUC 0.935 (95% CI 0.914 to 0.956)

  The review’s own words

  Similarly, in populations with alcohol-related stress, these models produced a pooled AUC of 0.935 (95% CIs 0.914–0.956, I 2 = 97.55%).

• PTSD prediction by ensemble methods in the general population

  AUC 0.841 (95% CI 0.783 to 0.898)

  The review’s own words

  In general PTSD populations, the meta-analysis included SVM, ensemble methods, and linear models, recording pooled AUCs of 0.824 (95% CIs 0.778–0.871, I 2 = 0%), 0.841 (95% CIs 0.783–0.898, I 2 = 0%), and 0.768 (95% CIs 0.608–0.928, I 2 = 60.06%), respectively.

• PTSD prediction by linear models in people with medical trauma

  AUC 0.828 (95% CI 0.717 to 0.939)

  The review’s own words

  In the context of medical trauma, analyses were conducted for two algorithms, revealing pooled AUCs of 0.828 (95% CIs 0.717–0.939, I 2 = 98.18%) for linear models and 0.808 (95% CIs 0.761–0.855, I 2 = 95.25%) for tree-based models.

• PTSD prediction by tree-based models in people with war or military experience

  AUC 0.745 (95% CI 0.572 to 0.917)

  The review’s own words

  Specifically, tree-based models achieved a pooled AUC of 0.745 (95% CIs 0.572–0.917, I 2 = 97.31%), compared to 0.615 (95% CIs 0.552–0.768, I 2 = 0%) for neural networks.

• PTSD prediction by tree-based models in people with sexual or physical trauma

  AUC 0.861 (95% CI 0.723 to 1)

  The review’s own words

  For individuals with experiences of sexual or physical trauma, the employment of tree-based models resulted in a pooled AUC of 0.861 (95% CIs 0.723–1, I 2 = 96.78%).

• PTSD prediction by tree-based models in people with medical trauma

  AUC 0.808 (95% CI 0.761 to 0.855)

  The review’s own words

  In the context of medical trauma, analyses were conducted for two algorithms, revealing pooled AUCs of 0.828 (95% CIs 0.717–0.939, I 2 = 98.18%) for linear models and 0.808 (95% CIs 0.761–0.855, I 2 = 95.25%) for tree-based models.

• PTSD prediction by tree-based models in firefighters

  AUC 0.96 (95% CI 0.918 to 1)

  The review’s own words

  Among firefighters, tree-based models achieved the highest pooled AUC across all examined demographics, at 0.96 (95% CIs 0.918–1, I 2 = 99.98%).

• PTSD prediction by tree-based models after natural disasters

  AUC 0.771 (95% CI 0.755 to 0.787)

  The review’s own words

  Tree-based models led with a pooled AUC of 0.771 (95% CIs 0.755–0.787, I 2 = 90.41%), closely followed by linear models with an AUC of 0.768 (95% CIs 0.764–0.771, I 2 = 78.76%).

• PTSD prediction by neural networks in people with war or military experience

  AUC 0.615 (95% CI 0.552 to 0.768)

  The review’s own words

  Specifically, tree-based models achieved a pooled AUC of 0.745 (95% CIs 0.572–0.917, I 2 = 97.31%), compared to 0.615 (95% CIs 0.552–0.768, I 2 = 0%) for neural networks.

• External validation of tree-based models predicting PTSD in people with medical trauma

  AUC 0.591 (95% CI 0.47 to 0.712)

  The review’s own words

  External validation of tree-based models in this demographic reported a pooled AUC of 0.591 (95% CIs 0.47–0.712, I 2 = 93.61%).

Code checks every number above against the review’s text before it is shown. The short labels are an AI model’s reading of that text, so the review’s own sentence sits under each one. A pooled figure is only as strong as the studies behind it. How Truvace uses pooled results