1. Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis.
- Author
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Vali M, Nezhad HM, Kovacs L, and Gandomi AH
- Subjects
- Humans, Algorithms, Stress Disorders, Post-Traumatic, Machine Learning
- 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 I
2 . 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., Competing Interests: Declarations. Ethics approval and consent to participate: Not applicable. This systematic review did not involve direct data collection from humans or animals, and, therefore, did not require ethics approval. Consent for publication: Not applicable. This manuscript does not contain any personal data from individuals. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)- Published
- 2025
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