Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review.
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 popula...
| Publicado en: | Journal of Affective Disorders Vol. 241; pp. 519 - 533 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , |
| Formato: | research systematic review Journal Article |
| Publicado: |
Elsevier B.V.
Dec2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=131628403&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 131628403 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01650327 3M9 jtl: Journal of Affective Disorders issn: 01650327 maglogo: N pubinfo: dt: Dec2018 vid: 241 pid: 1004 pub: Elsevier B.V. artinfo: ui: 131628403 131628403 NLM30153635 131628403 10.1016/j.jad.2018.08.073 NLM30153635 131628403 ppf: 519 ppct: 14 formats: tig: atl: Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review. aug: au: 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. affil: Institute of Medical Science, University of Toronto, Toronto, Canada sug: subj: Diagnosis, Computer Assisted Algorithms Antidepressive Agents Therapeutic Use Depression Drug Therapy Human Female Neuroradiography Retrospective Design Adult Depression Diagnosis Male Treatment Outcomes Meta Analysis Systematic Review Adult: 19-44 years Female Male ab: 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. pubtype: Academic Journal doctype: meta analysis research systematic review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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