A Deep Learning Approach for Predicting Antidepressant Response in Major Depression Using Clinical and Genetic Biomarkers
In the wake of recent advances in scientific research, personalized medicine using deep learning techniques represents a new paradigm. In this work, our goal was to establish deep learning models which distinguish responders from non-responders, and also to predict possible antidepressant treatment...
Main Authors: | Eugene Lin, Po-Hsiu Kuo, Yu-Li Liu, Younger W.-Y. Yu, Albert C. Yang, Shih-Jen Tsai |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2018-07-01
|
Series: | Frontiers in Psychiatry |
Subjects: | |
Online Access: | https://www.frontiersin.org/article/10.3389/fpsyt.2018.00290/full |
Similar Items
-
Gene-Based Association Analysis Suggests Association of HTR2A With Antidepressant Treatment Response in Depressed Patients
by: Chung-Feng Kao, et al.
Published: (2020-12-01) -
Prediction of Antidepressant Treatment Response and Remission Using an Ensemble Machine Learning Framework
by: Eugene Lin, et al.
Published: (2020-10-01) -
Role of Interleukin-6 in Depressive Disorder
by: Emily Yi-Chih Ting, et al.
Published: (2020-03-01) -
Prediction of Probable Major Depressive Disorder in the Taiwan Biobank: An Integrated Machine Learning and Genome-Wide Analysis Approach
by: Eugene Lin, et al.
Published: (2021-06-01) -
A Study of Antidepressant Prescription in Major Depressive Disorders in the Out-Patient Psychiatric Department of Siriraj Hospital
by: Somporn Wipisamakul, et al.
Published: (2005-08-01)