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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2015
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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. |
format | Online Article Text |
id | pubmed-4683422 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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