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 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.

Prediction of Probable Major Depressive Disorder in the Taiwan Biobank: An Integrated Machine Learning and Genome-Wide Analysis Approach

“…We performed the analyses for these five machine learning algorithms using WEKA software (which is available from accessed on 11 April 2021) [ 33 ] and a computer with Intel (R) Core (TM) i5-4210U, 4 GB RAM, and Windows 7. The tuning parameters of WEKA were determined at the specific values using a grid search approach [ 5 , 34 ].…”

“…In this higher dimensional space, the SVM model then finds a linear separating hyperplane with the maximal margin. In this study, we applied the polynomial kernel with the exponent value of 1.0 [ 5 ].…”

“…Advancements in machine learning models have shown promising potential in terms of personalized medicine for patients with psychiatric disorders [ 1 , 2 , 3 ]. For instance, machine learning models have been employed to derive clinical treatment outcomes in patients with major depressive disorder (MDD) [ 4 , 5 ] as well as disease status in patients with schizophrenia [ 6 , 7 ] using clinical characteristics and genetic variants such as single nucleotide polymorphisms (SNPs). Due to their wide range of potential applications, it has been suggested that machine learning models can play a pivotal role in the future of personalized medicine [ 8 , 9 , 10 ].…”

“…In previous studies [ 4 , 5 ], machine learning models were leveraged to estimate antidepressant treatment outcomes with the top-rated key SNPs acquired from a GWAS. Here, we investigated the feasibility of likely loci by performing a GWAS of probable MDD using a sample of 9828 Taiwanese individuals in the Taiwan Biobank.…”

Performance Assessment of Certain Machine Learning Models for Predicting the Major Depressive Disorder among IT Professionals during Pandemic times

“…A weighted average ensemble model was proposed for handling MDD [ 27 ]. In this analysis [ 28 ], the authors have analyzed heart rate variability to distinguish between diastolic heart failure and systolic heart failure patients. They have implemented the nearest neighbor and deep multilayered perceptron classifiers in evaluating the performances of classification.…”