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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Bibliographic Details
Main Authors:	Meenal J. Patel, Alexander Khalaf, Howard J. Aizenstein
Format:	Article
Language:	English
Published:	Elsevier 2016-01-01
Series:	NeuroImage: Clinical
Subjects:	Depression Machine learning Treatment Prediction Review
Online Access:	http://www.sciencedirect.com/science/article/pii/S2213158215300206

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spelling	doaj-7d3fe4c6a47a443cbab3b4c0b8ed1d5f2020-11-24T20:54:31ZengElsevierNeuroImage: Clinical2213-15822016-01-0110C11512310.1016/j.nicl.2015.11.003Studying depression using imaging and machine learning methodsMeenal J. Patel0Alexander Khalaf1Howard J. Aizenstein2Department of Bioengineering, University of Pittsburgh, PA, USAUniversity of Pittsburgh School of Medicine, PA, USADepartment of Bioengineering, University of Pittsburgh, PA, USADepression 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.http://www.sciencedirect.com/science/article/pii/S2213158215300206DepressionMachine learningTreatmentPredictionReview
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Meenal J. Patel Alexander Khalaf Howard J. Aizenstein
spellingShingle	Meenal J. Patel Alexander Khalaf Howard J. Aizenstein Studying depression using imaging and machine learning methods NeuroImage: Clinical Depression Machine learning Treatment Prediction Review
author_facet	Meenal J. Patel Alexander Khalaf Howard J. Aizenstein
author_sort	Meenal J. Patel
title	Studying depression using imaging and machine learning methods
title_short	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_sort	studying depression using imaging and machine learning methods
publisher	Elsevier
series	NeuroImage: Clinical
issn	2213-1582
publishDate	2016-01-01
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.
topic	Depression Machine learning Treatment Prediction Review
url	http://www.sciencedirect.com/science/article/pii/S2213158215300206
work_keys_str_mv	AT meenaljpatel studyingdepressionusingimagingandmachinelearningmethods AT alexanderkhalaf studyingdepressionusingimagingandmachinelearningmethods AT howardjaizenstein studyingdepressionusingimagingandmachinelearningmethods
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Studying depression using imaging and machine learning methods

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