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TRUVACE RECORD VERSION
record: TRV-2026-2753
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:19:21.096715Z
status: published
lens: p_space
sector: health
headline: Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis
dek: Abstract This study aimed to compare and evaluate the prediction accuracy and risk of bias (ROB) of post-traumatic stress disorder (PTSD) predictive models. 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 I2. The risk…
gain_title: (none)
problem_title: A systematic review of 23 machine learning studies predicting PTSD found none had low risk of bias (11 high, 12 unclear), and in medical-trauma populations external validation of tree-based models pooled an AUC of 0.591.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: A systematic review of 23 machine learning studies predicting PTSD found none had low risk of bias (11 high, 12 unclear), and in medical-trauma populations external validation of tree-based models pooled an AUC of 0.591.
problem_evidence: 48% of the 23 included studies had a high ROB | reported a pooled AUC of 0.591
quick_read: A systematic review and meta-analysis of machine learning models that predict post-traumatic stress disorder pooled results from 23 studies published from 2018 to December 2023. Using the PROBAST bias tool, the authors rated 48% of the studies (11) at high risk of bias and the other 12 as unclear; none were rated low.

Tree-based models were the most commonly used algorithm and scored highest on pooled AUC: 0.96 for firefighters, 0.935 for alcohol-related stress, 0.861 for sexual or physical trauma, 0.808 for medical trauma, 0.771 for natural disasters and 0.745 for military incidents. Heterogeneity was very high, with I2 values often above 90%.

Only two studies tested their models on outside data. Where external validation could be pooled, performance fell: tree-based models for medical trauma reached 0.808 internally but 0.591 externally. The authors say this limits how far the results apply to real clinical settings.
limitation: The authors say high heterogeneity among the included studies limits how the results can be interpreted. The review also excluded non-English papers and did not search EMBASE or the Cochrane Library, so some eligible studies may be missing.
tag: Evidence-backed problem
key_points: The review searched PubMed, Scopus and Web of Science for English-language studies from 2018 to 14 December 2023, screened 1,299 articles after removing duplicates, and included 18 studies plus 5 found through reference lists and Google, totalling 23. | Fifteen of the 23 studies (65%) came from the United States and four from China; 14 used tree-based models, eight used linear models and five each used SVMs and neural networks. | In war and military populations, tree-based models pooled an AUC of 0.745 against 0.615 for neural networks; in natural-disaster populations SVMs pooled only 0.593 against 0.771 for tree-based models. | The analysis domain carried the most bias: 43% of studies (10) were rated high risk, 52% unclear and one low, with small samples and excluded participants named as main causes. | Heterogeneity was often extreme, for example I2 of 99.98% for firefighters, though sensitivity analyses cut it to 0% for tree-based models in war populations. | Most studies did not report participants' racial background, which the authors say limits understanding of how well the models fit different groups. | The authors recommend following AI/ML reporting standards and PROBAST guidelines, and prioritising external validation before these models are used clinically.
rundown: The authors pooled area-under-the-curve scores separately for each trauma population and for each algorithm family, using random-effects models, and measured heterogeneity with I2. A pool needed at least two studies, so single-study results such as 0.75 for a random forest in commercially insured adults were reported without pooling. Two reviewers screened and assessed bias independently.

Reported accuracy varied widely by group. Pooled AUCs for tree-based models ranged from 0.745 for military incidents to 0.96 for firefighters. In general PTSD populations, ensemble methods (0.841) and SVMs (0.824) outperformed linear models (0.768). Individual studies reported AUCs from 0.46 to 0.99 depending on model and validation set.

External validation was rare: only two studies did it, both with tree-based models. In medical trauma the external pooled AUC of 0.591 was far below the internal 0.808, and a military study's external AUC of 0.74 was also below its development results. The authors attribute this to overfitting, small samples and differences between datasets.

The authors also note that a model needing 50 or more predictors may be impractical, so clinicians might prefer traditional tools such as the PCL-5. They add that testing effects on patient outcomes would need interventional trials and impact assessments.
sources:
- peer_reviewed | BMC Medical Informatics and Decision Making | https://doi.org/10.1186/s12911-024-02754-2 | 2025-01-21
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