Prediction of antidepressant treatment response and remission using an ensemble machine learning framework
Journal
Pharmaceuticals
Journal Volume
13
Journal Issue
10
Pages
1-12
Date Issued
2020
Author(s)
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 regression, support vector machine, C4.5 decision tree, na?ve Bayes, and random forests. Our data revealed that the ensemble predictive algorithm with feature selection (using fewer biomarkers) performed comparably to other predictive algorithms (such as MFNNs and logistic regression) to derive the perplexing relationship between biomarkers and the status of antidepressant treatments. Our study demonstrates that the ensemble machine learning framework may present a useful technique to create bioinformatics tools for discriminating non-responders from responders prior to antidepressant treatments. ? 2020 by the authors. Licensee MDPI, Basel, Switzerland.
Subjects
Antidepressant; Ensemble learning; Feature selection; Machine learning; Major depressive disorder; Pharmacogenomics; Single nucleotide polymorphisms
SDGs
Other Subjects
serotonin uptake inhibitor; algorithm; Article; benchmarking; bioinformatics; clinical feature; cohort analysis; conceptual framework; controlled study; drug efficacy; genetic variability; health status; human; machine learning; major clinical study; major depression; pharmacogenomics; population research; remission; single nucleotide polymorphism; support vector machine; treatment response
Publisher
MDPI AG
Type
journal article