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Open AccessArticle

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

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Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
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Department of Electrical & Computer Engineering, University of Washington, Seattle, WA 98195, USA
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Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
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Department of Public Health, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei 10617, Taiwan
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Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli County 35053, Taiwan
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Yu’s Psychiatric Clinic, Kaohsiung 802211, Taiwan
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Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA 02215, USA
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Institute of Brain Science, National Yang-Ming University, Taipei 112304, Taiwan
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Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11217, Taiwan
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Division of Psychiatry, National Yang-Ming University, Taipei 112304, Taiwan
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Authors to whom correspondence should be addressed.
Pharmaceuticals 2020, 13(10), 305; https://doi.org/10.3390/ph13100305
Received: 12 September 2020 / Revised: 8 October 2020 / Accepted: 12 October 2020 / Published: 13 October 2020
(This article belongs to the Special Issue The Pharmacogenomics of Mood Stabilizers)
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. View Full-Text
Keywords: antidepressant; ensemble learning; feature selection; machine learning; major depressive disorder; pharmacogenomics; single nucleotide polymorphisms antidepressant; ensemble learning; feature selection; machine learning; major depressive disorder; pharmacogenomics; single nucleotide polymorphisms
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Lin, E.; Kuo, P.-H.; Liu, Y.-L.; Yu, Y. .-Y.; Yang, A.C.; Tsai, S.-J. Prediction of Antidepressant Treatment Response and Remission Using an Ensemble Machine Learning Framework. Pharmaceuticals 2020, 13, 305.

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