Moving toward precision PTSD treatment: predicting veterans' intensive PTSD treatment response using continuously updating machine learning models.

ABSTRACT:

Background

Considerable heterogeneity exists in treatment response to first-line posttraumatic stress disorder (PTSD) treatments, such as Cognitive Processing Therapy (CPT). Relatively little is known about the timing of when during a course of care the treatment response becomes apparent. Novel machine learning methods, especially continuously updating prediction models, have the potential to address these gaps in our understanding of response and optimize PTSD treatment.

Methods

Using data from a 3-week (n = 362) CPT-based intensive PTSD treatment program (ITP), we explored three methods for generating continuously updating prediction models to predict endpoint PTSD severity. These included Mixed Effects Bayesian Additive Regression Trees (MixedBART), Mixed Effects

SUBMITTER: Smith DL 

PROVIDER: S-EPMC10482723 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature