Prediction of antidepressant treatment response and remission using an ensemble machine learning framework

Eugene Lin*, Po Hsiu Kuo, Yu Li Liu, Younger W.Y. Yu, Albert C. Yang, Shih Jen Tsai

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


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, naïve 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.

Original languageEnglish
Article number305
Pages (from-to)1-12
Number of pages12
Issue number10
StatePublished - Oct 2020


  • Antidepressant
  • Ensemble learning
  • Feature selection
  • Machine learning
  • Major depressive disorder
  • Pharmacogenomics
  • Single nucleotide polymorphisms


Dive into the research topics of 'Prediction of antidepressant treatment response and remission using an ensemble machine learning framework'. Together they form a unique fingerprint.

Cite this