Development and validation of a meta-learner for combining statistical and machine learning prediction models in individuals with depression.

ABSTRACT:

Background

The debate of whether machine learning models offer advantages over standard statistical methods when making predictions is ongoing. We discuss the use of a meta-learner model combining both approaches as an alternative.

Methods

To illustrate the development of a meta-learner, we used a dataset of 187,757 people with depression. Using 31 variables, we aimed to predict two outcomes measured 60 days after initiation of antidepressant treatment: severity of depressive symptoms (continuous) and all-cause dropouts (binary). We fitted a ridge regression and a multi-layer perceptron (MLP) deep neural network as two separate prediction models ("base-learners"). We then developed two "meta-learners", combining predictions from the two base-learners. To compare the performa

SUBMITTER: Liu Q 

PROVIDER: S-EPMC9112573 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature