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Studying depression using imaging and machine learning methods

Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize...

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Detalles Bibliográficos
Autores principales: Patel, Meenal J., Khalaf, Alexander, Aizenstein, Howard J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4683422/
https://www.ncbi.nlm.nih.gov/pubmed/26759786
http://dx.doi.org/10.1016/j.nicl.2015.11.003
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author Patel, Meenal J.
Khalaf, Alexander
Aizenstein, Howard J.
author_facet Patel, Meenal J.
Khalaf, Alexander
Aizenstein, Howard J.
author_sort Patel, Meenal J.
collection PubMed
description Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presents a background on depression, imaging, and machine learning methodologies; (2) reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3) suggests directions for future depression-related studies.
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spelling pubmed-46834222016-01-12 Studying depression using imaging and machine learning methods Patel, Meenal J. Khalaf, Alexander Aizenstein, Howard J. Neuroimage Clin Review Article Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presents a background on depression, imaging, and machine learning methodologies; (2) reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3) suggests directions for future depression-related studies. Elsevier 2015-11-10 /pmc/articles/PMC4683422/ /pubmed/26759786 http://dx.doi.org/10.1016/j.nicl.2015.11.003 Text en © 2015 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Review Article
Patel, Meenal J.
Khalaf, Alexander
Aizenstein, Howard J.
Studying depression using imaging and machine learning methods
title Studying depression using imaging and machine learning methods
title_full Studying depression using imaging and machine learning methods
title_fullStr Studying depression using imaging and machine learning methods
title_full_unstemmed Studying depression using imaging and machine learning methods
title_short Studying depression using imaging and machine learning methods
title_sort studying depression using imaging and machine learning methods
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4683422/
https://www.ncbi.nlm.nih.gov/pubmed/26759786
http://dx.doi.org/10.1016/j.nicl.2015.11.003
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