Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review

•We surveyed the use of machine learning to inform predictive models in mood disorders.•We include studies that use machine learning algorithms to identify predictors of therapeutic outcomes in uni/bipolar depression.•Classification algorithms informed by neuroimaging, phenomenological, and genetic...

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Bibliographic Details
Published in:Journal of affective disorders 2018-12, Vol.241, p.519-532
Main Authors: Lee, Yena, Ragguett, Renee-Marie, Mansur, Rodrigo B., Boutilier, Justin J., Rosenblat, Joshua D., Trevizol, Alisson, Brietzke, Elisa, Lin, Kangguang, Pan, Zihang, Subramaniapillai, Mehala, Chan, Timothy C.Y., Fus, Dominika, Park, Caroline, Musial, Natalie, Zuckerman, Hannah, Chen, Vincent Chin-Hung, Ho, Roger, Rong, Carola, McIntyre, Roger S.
Format: Article
Language:English
Subjects:
Adult
Algorithms
Antidepressive Agents - therapeutic use
Artificial intelligence
Automated pattern recognition
Bipolar disorder
Depressive Disorder - diagnosis
Depressive Disorder - drug therapy
Diagnosis, Computer-Assisted
Female
Humans
Machine Learning
Major depressive disorder
Male
Mood disorders
Neural networks (computer)
Neuroimaging
Retrospective Studies
Treatment Outcome
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Summary:•We surveyed the use of machine learning to inform predictive models in mood disorders.•We include studies that use machine learning algorithms to identify predictors of therapeutic outcomes in uni/bipolar depression.•Classification algorithms informed by neuroimaging, phenomenological, and genetic data were able to predict therapeutic outcomes with an overall accuracy of 0.82.•Predictive models integrating multiple data types performed better when compared to models with single lower-dimension data types (p
ISSN:0165-0327
1573-2517
DOI:10.1016/j.jad.2018.08.073