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  1. NTU Scholars
  2. 公共衛生學院
  3. 流行病學與預防醫學研究所
Please use this identifier to cite or link to this item: https://scholars.lib.ntu.edu.tw/handle/123456789/521009
Title: Prediction of antidepressant treatment response and remission using an ensemble machine learning framework
Authors: Lin E.
PO-HSIU KUO 
Liu Y.-L.
Yu Y.W.-Y.
Yang A.C.
Tsai S.-J.
Keywords: Antidepressant; Ensemble learning; Feature selection; Machine learning; Major depressive disorder; Pharmacogenomics; Single nucleotide polymorphisms
Issue Date: 2020
Publisher: MDPI AG
Journal Volume: 13
Journal Issue: 10
Start page/Pages: 1-12
Source: Pharmaceuticals
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.
URI: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092519757&doi=10.3390%2fph13100305&partnerID=40&md5=36f459dc1bf535ad5c92891d6a96340a
https://scholars.lib.ntu.edu.tw/handle/123456789/521009
ISSN: 1424-8247
DOI: 10.3390/ph13100305
SDG/Keyword: 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
[SDGs]SDG3
Appears in Collections:流行病學與預防醫學研究所

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臺大位居世界頂尖大學之列,為永久珍藏及向國際展現本校豐碩的研究成果及學術能量,圖書館整合機構典藏(NTUR)與學術庫(AH)不同功能平台,成為臺大學術典藏NTU scholars。期能整合研究能量、促進交流合作、保存學術產出、推廣研究成果。

To permanently archive and promote researcher profiles and scholarly works, Library integrates the services of “NTU Repository” with “Academic Hub” to form NTU Scholars.

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