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Differential Treatment Benefit Prediction for Treatment Selection in Depression: A Deep Learning Analysis of STAR*D and CO-MED Data Cover

Differential Treatment Benefit Prediction for Treatment Selection in Depression: A Deep Learning Analysis of STAR*D and CO-MED Data

Open Access
|Jan 2020

Authors

Joseph Mehltretter

info@ubiquitypress.com

Department of Computer Science, University of Southern California, Los Angeles, California, US; Aifred Health, Montreal, Quebec

Robert Fratila

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Aifred Health, Montreal, Quebec

David A. Benrimoh

david.benrimoh@mail.mcgill.ca

Aifred Health, Montreal, Quebec; Department of Psychiatry, McGill University, Montreal, Quebec

Adam Kapelner

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Department of Mathematics, Queen’s College, New York City, NY

Kelly Perlman

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Aifred Health, Montreal, Quebec; Department of Psychiatry, McGill University, Montreal, Quebec

Emily Snook

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Aifred Health, Montreal, Quebec; Department of Psychiatry, McGill University, Montreal, Quebec

Sonia Israel

info@ubiquitypress.com

Aifred Health, Montreal, Quebec; Department of Psychiatry, McGill University, Montreal, Quebec

Caitrin Armstrong

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Aifred Health, Montreal, Quebec

Marc Miresco

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Department of Psychiatry, McGill University, Montreal, Quebec

Gustavo Turecki

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Department of Psychiatry, McGill University, Montreal, Quebec
Language: English
Submitted on: May 16, 2019
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Accepted on: Oct 19, 2020
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Published on: Jan 1, 2020
Published by: MIT Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2020 Joseph Mehltretter, Robert Fratila, David A. Benrimoh, Adam Kapelner, Kelly Perlman, Emily Snook, Sonia Israel, Caitrin Armstrong, Marc Miresco, Gustavo Turecki, published by MIT Press
This work is licensed under the Creative Commons Attribution 4.0 License.