Machine learning in the prediction of treatment response for emotional disorders: A systematic review and meta-analysis

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This systematic review and meta-analysis evaluates the accuracy of machine learning algorithms in predicting treatment response for emotional disorders such as depression and anxiety. The authors synthesize findings from 155 studies to assess overall prediction performance and identify moderators, including the impact of cross-validation rigor and predictor types like neuroimaging data. The resource is intended for researchers and clinicians interested in the methodological aspects and current evidence regarding predictive modeling in mental health treatment.

ID
524dd30c98f448ae
Source org
PubMed Central
Author
Joshua Curtiss (Northeastern University), Christopher P. DiPietro (Cognitive Research (United States))
Published
2025-05-22
OA status
hybrid
DOI
10.1016/j.cpr.2025.102593
PMID
Not recorded
PMCID
Not recorded
Citations
16

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BibTeX

@article{allodium:524dd30c98f448ae,
  title = {Machine learning in the prediction of treatment response for emotional disorders: A systematic review and meta-analysis},
  author = {Joshua Curtiss and Christopher P. DiPietro},
  year = {2025},
  journal = {PubMed Central},
  doi = {10.1016/j.cpr.2025.102593},
  url = {https://doi.org/10.1016/j.cpr.2025.102593}
}

RIS

TY  - JOUR
TI  - Machine learning in the prediction of treatment response for emotional disorders: A systematic review and meta-analysis
AU  - Curtiss, Joshua
AU  - DiPietro, Christopher P.
PY  - 2025
PB  - PubMed Central
DO  - 10.1016/j.cpr.2025.102593
UR  - https://doi.org/10.1016/j.cpr.2025.102593
ER  -

APA

Curtiss, J., & DiPietro, C. P. (2025). Machine learning in the prediction of treatment response for emotional disorders: A systematic review and meta-analysis. PubMed Central. https://doi.org/10.1016/j.cpr.2025.102593

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