Open AccessDeep Learning: A New Horizon for Personalized Treatment of Depression?
Author(s) -
David Benrimoh,
Robert Fratila,
Sonia Israel,
Kelly Perlman
Publication year - 2018
Publication title -
mcgill journal of medicine
Language(s) - English
Resource type - Journals
eISSN - 1715-8125
pISSN - 1201-026X
DOI - 10.26443/mjm.v16i1.99
Subject(s) - medicine , depression (economics) , personalization , construct (python library) , personalized medicine , artificial intelligence , deep learning , machine learning , psychiatry , bioinformatics , computer science , biology , world wide web , economics , macroeconomics , programming language
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.