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Machine learning in the prediction of depression treatment outcomes: a systematic review and meta-analysis

Published online by Cambridge University Press:  12 October 2021

Mehri Sajjadian ,
Raymond W. Lam ,
Roumen Milev ,
Susan Rotzinger ,
Benicio N. Frey ,
Claudio N. Soares ,
Sagar V. Parikh ,
Jane A. Foster ,
Gustavo Turecki  and
Daniel J. Müller
...Show all authors
Mehri Sajjadian
Affiliation:
Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
Raymond W. Lam
Affiliation:
Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
Roumen Milev
Affiliation:
Department of Psychiatry and Psychology, Queen's University, Providence Care Hospital, Kingston, ON, Canada
Susan Rotzinger
Affiliation:
Department of Psychiatry, University of Toronto, Toronto, ON, Canada Department of Psychiatry, St. Michael's Hospital, University of Toronto, Toronto, Ontario, Canada
Benicio N. Frey
Affiliation:
Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada Mood Disorders Program and Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada
Claudio N. Soares
Affiliation:
Department of Psychiatry, Queen's University School of Medicine, Kingston, ON, Canada
Sagar V. Parikh
Affiliation:
Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
Jane A. Foster
Affiliation:
Department of Psychiatry & Behavioural Neurosciences, St. Joseph's Healthcare, Hamilton, ON, Canada
Gustavo Turecki
Affiliation:
Department of Psychiatry, Douglas Institute, McGill University, Montreal, QC, Canada
Daniel J. Müller
Affiliation:
Campbell Family Mental Health Research Institute, Center for Addiction and Mental Health, Toronto, ON, Canada Department of Psychiatry, University of Toronto, Toronto, ON, Canada
Stephen C. Strother
Affiliation:
Baycrest and Department of Medical Biophysics, Rotman Research Center, University of Toronto, Toronto, ON, Canada
Faranak Farzan
Affiliation:
eBrain Lab, School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC, Canada
Sidney H. Kennedy
Affiliation:
Department of Psychiatry, University of Toronto, Toronto, ON, Canada Department of Psychiatry, St. Michael's Hospital, University of Toronto, Toronto, Ontario, Canada Department of Psychiatry, University Health Network, Toronto, ON, Canada Krembil Research Centre, University Health Network, University of Toronto, Toronto, ON, Canada
Rudolf Uher*
Affiliation:
Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
*
Author for correspondence: Rudolf Uher, E-mail: uher@dal.ca
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Abstract

Background

Multiple treatments are effective for major depressive disorder (MDD), but the outcomes of each treatment vary broadly among individuals. Accurate prediction of outcomes is needed to help select a treatment that is likely to work for a given person. We aim to examine the performance of machine learning methods in delivering replicable predictions of treatment outcomes.

Methods

Of 7732 non-duplicate records identified through literature search, we retained 59 eligible reports and extracted data on sample, treatment, predictors, machine learning method, and treatment outcome prediction. A minimum sample size of 100 and an adequate validation method were used to identify adequate-quality studies. The effects of study features on prediction accuracy were tested with mixed-effects models. Fifty-four of the studies provided accuracy estimates or other estimates that allowed calculation of balanced accuracy of predicting outcomes of treatment.

Results

Eight adequate-quality studies reported a mean accuracy of 0.63 [95% confidence interval (CI) 0.56–0.71], which was significantly lower than a mean accuracy of 0.75 (95% CI 0.72–0.78) in the other 46 studies. Among the adequate-quality studies, accuracies were higher when predicting treatment resistance (0.69) and lower when predicting remission (0.60) or response (0.56). The choice of machine learning method, feature selection, and the ratio of features to individuals were not associated with reported accuracy.

Conclusions

The negative relationship between study quality and prediction accuracy, combined with a lack of independent replication, invites caution when evaluating the potential of machine learning applications for personalizing the treatment of depression.

Keywords

Machine learningMDDmeta-analysispredictive analysissystematic reviewtreatment outcome
Type
Review Article
Information
Psychological Medicine , Volume 51 , Issue 16 , December 2021 , pp. 2742 - 2751
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

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