lancet-header
Preprints with The Lancet is part of SSRN´s First Look, a place where journals and other research experts identify content of interest prior to publication. These preprint papers are not peer-reviewed and are posted here as part of a 12-month trial. Authors have either opted in at submission to The Lancet family of journals to post their preprints on Preprints with The Lancet, or submitted directly via SSRN. The usual SSRN checks and a Lancet-specific check for appropriateness and transparency have been applied. These papers should not be used for clinical decision making or reporting of research to a lay audience without indicating that this is preliminary research that has not been peer-reviewed. For more information see the Comment published in The Lancet, or visit The Lancet´s FAQ page, and for any feedback please contact preprints@lancet.com

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

16 Pages Posted: 13 Feb 2019

See all articles by Joseph Mehltretter

Joseph Mehltretter

affiliation not provided to SSRN

Robert Fratila

affiliation not provided to SSRN

David Benrimoh

McGill University

Adam Kapelner

affiliation not provided to SSRN

Kelly Perlman

affiliation not provided to SSRN

Emily Snook

affiliation not provided to SSRN

Sonia Israel

affiliation not provided to SSRN

Gustavo Turecki

McGill University - McGill Group for Suicide Studies; Aix-Marseille University - Assistance Publique-Hopitaux de Marseille

More...

Abstract

Background: Depression affects one in nine people, but treatment response rates remain low. There is significant potential in the use of computational modelling techniques to predict individual patient responses and thus provide more personalized treatment. Deep learning is a promising computational technique that can be used for differential treatment selection based on predicted remission probability.    

Methods: Using STAR*D and CO-MED trial data, we employed deep neural networks to predict remission after feature reduction. Differential treatment benefit was estimated in terms of improvement of population remission rates after application of the model for treatment selection using both naive and conservative approaches. The naive approach assessed population remission rate in five sets of 200 patients held apart from the training set; the conservative approach used bootstrapping for sample generation and focused on population remission rate for patients who actually received the drug predicted by the model compared to the general population.    

Outcomes: Our deep learning model predicted remission in a pooled CO-MED/STAR*D dataset (including four treatments) with an AUC of 0.69 using 17 input features. Our naive analysis showed an improvement of remission of over 30% (from a 34.33% population remission rate to 46.12%). Our conservative analysis showed a 7.2% relative improvement in population remission rate (p= 0.01, C.I. 2.48% ± .5%).

Interpretation: Our model serves as proof-of-concept that deep learning has utility in differential prediction of antidepressant response when selecting from more than two treatment options. These models may have significant real-world clinical implications.    

Funding: Aifred Health  

Declaration of Interest: Dr. Benrimoh reports other from Aifred Health, during the conduct of the study; other from Aifred Health, outside the submitted work; All other authors declare none.

Ethical Approval: This work was approved by the Research Ethics Committee of the Douglas Mental Health University Institute.

Keywords: Treatment selection, differential treatment selection, depression, AI, machine learning, modelling

Suggested Citation

Mehltretter, Joseph and Fratila, Robert and Benrimoh, David and Kapelner, Adam and Perlman, Kelly and Snook, Emily and Israel, Sonia and Turecki, Gustavo, Differential Treatment Benefit Prediction for Treatment Selection in Depression: A Deep Learning Analysis of STAR*D and CO-MED Data (December 29, 2018). Available at SSRN: https://ssrn.com/abstract=3309427

Joseph Mehltretter

affiliation not provided to SSRN

Robert Fratila

affiliation not provided to SSRN

David Benrimoh (Contact Author)

McGill University ( email )

1001 Sherbrooke St. W
Montreal, Quebec H3A 1G5
Canada

Adam Kapelner

affiliation not provided to SSRN

Kelly Perlman

affiliation not provided to SSRN

Emily Snook

affiliation not provided to SSRN

Sonia Israel

affiliation not provided to SSRN

Gustavo Turecki

McGill University - McGill Group for Suicide Studies ( email )

Canada

Aix-Marseille University - Assistance Publique-Hopitaux de Marseille ( email )

Marseille
France

Click here to go to TheLancet.com

Go to TheLancet.com

Paper statistics

Abstract Views
560
Downloads
65