Analysis of Features Selected by a Deep Learning Model for Differential Treatment Selection in Depression

Analysis of Features Selected by a Deep Learning Model for Differential Treatment Selection in Depression

Background: Deep learning has utility in predicting differential antidepressant treatment response among patients with major depressive disorder, yet there remains a paucity of research describing how to interpret deep learning models in a clinically or etiologically meaningful way. In this paper, w...

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Bibliographic Details
Main Authors: Joseph Mehltretter, Colleen Rollins, David Benrimoh, Robert Fratila, Kelly Perlman, Sonia Israel, Marc Miresco, Marina Wakid, Gustavo Turecki
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-01-01
Series:Frontiers in Artificial Intelligence
Subjects:
deep learning
features
depression
interpretability
treatment
Online Access:https://www.frontiersin.org/article/10.3389/frai.2019.00031/full

Internet

https://www.frontiersin.org/article/10.3389/frai.2019.00031/full

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