Methodological choices and clinical usefulness for machine learning predictions of outcome in Internet-based cognitive behavioural therapy.

ABSTRACT:

Background

While psychological treatments are effective, a substantial portion of patients do not benefit enough. Early identification of those may allow for adaptive treatment strategies and improved outcomes. We aimed to evaluate the clinical usefulness of machine-learning (ML) models predicting outcomes in Internet-based Cognitive Behavioural Therapy, to compare ML-related methodological choices, and guide future use of these.

Methods

Eighty main models were compared. Baseline variables, weekly symptoms, and treatment activity were used to predict treatment outcomes in a dataset of 6695 patients from regular care.

Results

We show that the best models use handpicked predictors and impute missing data. No ML algorithm shows clear superiority. They have a mean balance

SUBMITTER: Hentati Isacsson N 

PROVIDER: S-EPMC11464669 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature