Modifiable predictors of suicidal ideation during psychotherapy for late-life major depression. A machine learning approach.

ABSTRACT: This study aimed to identify subgroups of depressed older adults with distinct trajectories of suicidal ideation during brief psychotherapy and to detect modifiable predictors of membership to the trajectories of suicidal ideation. Latent growth mixed models were used to identify trajectories of the presence of suicidal ideation in participants to a randomized controlled trial comparing Problem Solving Therapy with "Engage" therapy in older adults with major depression over 9 weeks. Predictors of membership to trajectories of suicidal ideation were identified by the convergence of four machine learning models, i.e., least absolute shrinkage and selection operator logistic regression, random forest, gradient boosting machine, and classification tree. The course of suicidal ideation was best

SUBMITTER: Alexopoulos GS 

PROVIDER: S-EPMC8523563 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature