Development and proof-of-concept of a treatment target recommendation algorithm in the context of cognitive processing therapy

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This paper describes a proof-of-concept study developing a machine learning algorithm to help clinicians identify specific maladaptive trauma-related beliefs (stuck points) to target during Cognitive Processing Therapy for PTSD. Using data from 898 veterans and service members, the researchers utilized clustering and regression models to find which cognitive items predicted symptom reduction within patient clusters. The resource outlines how this data-driven approach aims to personalize treatment by recommending specific beliefs to restructure, with simulated cases suggesting potential additional symptom improvement.

ID
37a9dba759c94ae9
Source org
PubMed Central
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OA status
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DOI
10.1080/20008066.2026.2623713
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BibTeX

@article{allodium:37a9dba759c94ae9,
  title = {Development and proof-of-concept of a treatment target recommendation algorithm in the context of cognitive processing therapy},
  journal = {PubMed Central},
  doi = {10.1080/20008066.2026.2623713},
  url = {https://doi.org/10.1080/20008066.2026.2623713}
}

RIS

TY  - JOUR
TI  - Development and proof-of-concept of a treatment target recommendation algorithm in the context of cognitive processing therapy
PB  - PubMed Central
DO  - 10.1080/20008066.2026.2623713
UR  - https://doi.org/10.1080/20008066.2026.2623713
ER  -

APA

Development and proof-of-concept of a treatment target recommendation algorithm in the context of cognitive processing therapy. (n.d.). PubMed Central. https://doi.org/10.1080/20008066.2026.2623713

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