Combining machine learning algorithms for prediction of antidepressant treatment response.

ABSTRACT:

Objectives

Predictors for unfavorable treatment outcome in major depressive disorder (MDD) applicable for treatment selection are still lacking. The database of a longitudinal multicenter study on 1079 acutely depressed patients, performed by the German research network on depression (GRND), allows supervised and unsupervised learning to further elucidate the interplay of clinical and psycho-sociodemographic variables and their predictive impact on treatment outcome phenotypes.

Experimental procedures

Treatment response was defined by a change of HAM-D 17-item baseline score ≥50% and remission by the established threshold of ≤7, respectively, after up to eight weeks of inpatient treatment. After hierarchical symptom clustering and stratification by treatment subtypes (seroto

SUBMITTER: Kautzky A 

PROVIDER: S-EPMC7839691 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature