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

• Published in: Psychological medicine Vol. 51; no. 16; pp. 2742 - 2751
• Main Authors: Sajjadian, Mehri, Lam, Raymond W., Milev, Roumen, Rotzinger, Susan, Frey, Benicio N., Soares, Claudio N., Parikh, Sagar V., Foster, Jane A., Turecki, Gustavo, Müller, Daniel J., Strother, Stephen C., Farzan, Faranak, Kennedy, Sidney H., Uher, Rudolf
• Format: Journal Article
• Language: English
• Published: Cambridge, UK Cambridge University Press 01.12.2021
• Subjects: Accuracy; Adequacy; Antidepressants; Choice learning; Clinical outcomes; Depression; Depressive Disorder, Major - therapy; Depressive personality disorders; Estimates; Feature selection; Humans; Learning algorithms; Machine Learning; Mental depression; Meta-analysis; Predictions; Prognosis; Remission; Remission (Medicine); Systematic review; Treatment Outcome; Treatment resistance; meta-analysis; MDD; systematic review; treatment outcome; Machine learning; predictive analysis
• ISSN: 0033-2917; 1469-8978; 1469-8978
• DOI: 10.1017/S0033291721003871

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. 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. 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. 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.