Replication of machine learning methods to predict treatment outcome with antidepressant medications in patients with major depressive disorder from STAR*D and CAN-BIND-1.

ABSTRACT:

Objectives

Antidepressants are first-line treatments for major depressive disorder (MDD), but 40-60% of patients will not respond, hence, predicting response would be a major clinical advance. Machine learning algorithms hold promise to predict treatment outcomes based on clinical symptoms and episode features. We sought to independently replicate recent machine learning methodology predicting antidepressant outcomes using the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) dataset, and then externally validate these methods to train models using data from the Canadian Biomarker Integration Network in Depression (CAN-BIND-1) dataset.

Methods

We replicated methodology from Nie et al (2018) using common algorithms based on linear regressions and decision trees

SUBMITTER: Nunez JJ 

PROVIDER: S-EPMC8238228 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature