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...
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| Format: | Article |
| Language: | English |
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BMC
2025-01-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-024-02754-2 |
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| author | Masoumeh Vali Hossein Motahari Nezhad Levente Kovacs Amir H Gandomi |
| author_facet | Masoumeh Vali Hossein Motahari Nezhad Levente Kovacs Amir H Gandomi |
| author_sort | Masoumeh Vali |
| collection | DOAJ |
| description | 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 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. |
| format | Article |
| id | doaj-art-41ae507aacc34aeca44c29e6eb68f0d9 |
| institution | Kabale University |
| issn | 1472-6947 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-41ae507aacc34aeca44c29e6eb68f0d92025-01-26T12:36:55ZengBMCBMC Medical Informatics and Decision Making1472-69472025-01-0125111610.1186/s12911-024-02754-2Machine learning algorithms for predicting PTSD: a systematic review and meta-analysisMasoumeh Vali0Hossein Motahari Nezhad1Levente Kovacs2Amir H Gandomi3Doctoral School of Applied Informatics and Applied Mathematics, Obuda UniversityObuda UniversityPhysiological Controls Research Center, University Research and Innovation Center, Obuda UniversityFaculty of Engineering and Information Technology, University of Technology SydneyAbstract 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 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.https://doi.org/10.1186/s12911-024-02754-2TraumaMental healthModel evaluationEvidence synthesisDeep learningForecasting |
| spellingShingle | Masoumeh Vali Hossein Motahari Nezhad Levente Kovacs Amir H Gandomi Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis BMC Medical Informatics and Decision Making Trauma Mental health Model evaluation Evidence synthesis Deep learning Forecasting |
| title | Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis |
| title_full | Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis |
| title_fullStr | Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis |
| title_full_unstemmed | Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis |
| title_short | Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis |
| title_sort | machine learning algorithms for predicting ptsd a systematic review and meta analysis |
| topic | Trauma Mental health Model evaluation Evidence synthesis Deep learning Forecasting |
| url | https://doi.org/10.1186/s12911-024-02754-2 |
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