Prediction of Antidepressant Treatment Response and Remission Using an Ensemble Machine Learning Framework.

ABSTRACT: In the wake of recent advances in machine learning research, the study of pharmacogenomics using predictive algorithms serves as a new paradigmatic application. In this work, our goal was to explore an ensemble machine learning approach which aims to predict probable antidepressant treatment response and remission in major depressive disorder (MDD). To discover the status of antidepressant treatments, we established an ensemble predictive model with a feature selection algorithm resulting from the analysis of genetic variants and clinical variables of 421 patients who were treated with selective serotonin reuptake inhibitors. We also compared our ensemble machine learning framework with other state-of-the-art models including multi-layer feedforward neural networks (MFNNs), logistic regres

SUBMITTER: Lin E 

PROVIDER: S-EPMC7599952 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature