Using machine learning methods to predict the outcome of psychological therapies for post-traumatic stress disorder: A systematic review.

Journal: Journal of anxiety disorders

Published Date: Mar 11, 2025

Abstract

BACKGROUND: A number of treatments are available for post-traumatic stress disorder (PTSD), however, there is currently a lack of data-driven treatment selection and adaptation methods for this condition. Machine learning (ML) could potentially help to improve the prediction of treatment outcomes and enable precision mental healthcare in practice.

Authors

James Tait

School of Psychology, University of Sheffield, ICOSS Building, 219 Portobello, Sheffield S1 4DP, United Kingdom; Department of Health Sciences, University of York, Seebohm Rowntree Building, Heslington, York YO10 5DD, United Kingdom. Electronic address: james.tait@york.ac.uk.
Stephen Kellett

Grounded Research, RDaSH NHS Foundation Trust, 2 St Catherine's Close, Tickhill Road Hospital, Balby, Doncaster DN4 8QN, United Kingdom.
Jaime Delgadillo

Clinical Psychology Unit, Department of Psychology, University of Sheffield.

Keywords

Humans Machine Learning Outcome Assessment, Health Care Psychotherapy Stress Disorders, Post-Traumatic

External Resources

View on PubMed Access via DOI PubMed (40132235)

Using machine learning methods to predict the outcome of psychological therapies for post-traumatic stress disorder: A systematic review.

Abstract

Authors

Keywords

External Resources

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