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

Yena Lee, Renee Marie Ragguett, Rodrigo B. Mansur, Justin J. Boutilier, Joshua D. Rosenblat, Alisson Trevizol, Elisa Brietzke, Kangguang Lin, Zihang Pan, Mehala Subramaniapillai, Timothy C.Y. Chan, Dominika Fus, Caroline Park, Natalie Musial, Hannah Zuckerman, Vincent Chin Hung Chen, Roger Ho, Carola Rong, Roger S. McIntyre*

*此作品的通信作者

研究成果: 期刊稿件文獻綜述同行評審

203 引文 斯高帕斯(Scopus)

摘要

Background: No previous study has comprehensively reviewed the application of machine learning algorithms in mood disorders populations. Herein, we qualitatively and quantitatively evaluate previous studies of machine learning-devised models that predict therapeutic outcomes in mood disorders populations. Methods: We searched Ovid MEDLINE/PubMed from inception to February 8, 2018 for relevant studies that included adults with bipolar or unipolar depression; assessed therapeutic outcomes with a pharmacological, neuromodulatory, or manual-based psychotherapeutic intervention for depression; applied a machine learning algorithm; and reported predictors of therapeutic response. A random-effects meta-analysis of proportions and meta-regression analyses were conducted. Results: We identified 639 records: 75 full-text publications were assessed for eligibility; 26 studies (n=17,499) and 20 studies (n=6325) were included in qualitative and quantitative review, respectively. Classification algorithms were able to predict therapeutic outcomes with an overall accuracy of 0.82 (95% confidence interval [CI] of [0.77, 0.87]). Pooled estimates of classification accuracy were significantly greater (p < 0.01) in models informed by multiple data types (e.g., composite of phenomenological patient features and neuroimaging or peripheral gene expression data; pooled proportion [95% CI] = 0.93[0.86, 0.97]) when compared to models with lower-dimension data types (pooledproportion=0.68[0.62,0.74]to0.85[0.81,0.88]). Limitations: Most studies were retrospective; differences in machine learning algorithms and their implementation (e.g., cross-validation, hyperparameter tuning); cannot infer importance of individual variables fed into learning algorithm. Conclusions: Machine learning algorithms provide a powerful conceptual and analytic framework capable of integrating multiple data types and sources. An integrative approach may more effectively model neurobiological components as functional modules of pathophysiology embedded within the complex, social dynamics that influence the phenomenology of mental disorders.

原文英語
頁(從 - 到)519-532
頁數14
期刊Journal of Affective Disorders
241
DOIs
出版狀態已出版 - 01 12 2018

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© 2018

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