Vol. 16 No. 1 (2018)
Reflections

Deep Learning: A New Horizon for Personalized Treatment of Depression?

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  • David Benrimoh,
  • Robert Fratila,
  • Sonia Israel,
  • Kelly Perlman
David Benrimoh
McGill University University College London
Bio
Robert Fratila
McGill University
Bio
Sonia Israel
McGill University
Bio
Kelly Perlman
McGill University
Bio
DOI: https://doi.org/10.26443/mjm.v16i1.99

Published 2018-07-06

Keywords

  • depression,
  • personalized medicine,
  • artificial intelligence

How to Cite

1.
Benrimoh D, Fratila R, Israel S, Perlman K. Deep Learning: A New Horizon for Personalized Treatment of Depression?. McGill J Med [Internet]. 2018 Jul. 6 [cited 2025 Oct. 14];16(1). Available from: https://mjm.mcgill.ca/article/view/99

Copyright (c) 2020 McGill Journal of Medicine

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

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Abstract

Globally, depression affects 300 million people and is projected be the leading cause of disability by 2030. While different patients are known to benefit from different therapies, there is no principled way for clinicians to predict individual patient responses or side effect profiles. A form of machine learning based on artificial neural networks, deep learning, might be useful for generating a predictive model that could aid in clinical decision making. Such a model’s primary outcomes would be to help clinicians select the most effective treatment plans and mitigate adverse side effects, allowing doctors to provide greater personalized care to a larger number of patients. In this commentary, we discuss the need for personalization of depression treatment and how a deep learning model might be used to construct a clinical decision aid.


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