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

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 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

SUBMITTER: Vali M 

PROVIDER: S-EPMC11752770 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature