A machine learning ensemble to predict treatment outcomes following an Internet intervention for depression.

ABSTRACT:

Background

Some Internet interventions are regarded as effective treatments for adult depression, but less is known about who responds to this form of treatment.

Method

An elastic net and random forest were trained to predict depression symptoms and related disability after an 8-week course of an Internet intervention, Deprexis, involving adults (N = 283) from across the USA. Candidate predictors included psychopathology, demographics, treatment expectancies, treatment usage, and environmental context obtained from population databases. Model performance was evaluated using predictive R2$\lpar R_{{\rm pred}}^2\rpar\comma $ the expected variance explained in a new sample, estimated by 10 repetitions of 10-fold cross-validation.

Results

An ensemble model was created by

SUBMITTER: Pearson R 

PROVIDER: S-EPMC6763538 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature