期刊
NEUROIMAGE-CLINICAL
卷 10, 期 -, 页码 115-123出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.nicl.2015.11.003
关键词
Depression; Machine learning; Treatment; Prediction; Review
类别
资金
- NIH [R01MH076079]
- University of Pittsburgh Clinical Scientist Training Program [UL1 TL1TR000005]
- NIMH Medical Student Research Fellowship [R25 MH054318-18]
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. (C) 2015 The Authors. Published by Elsevier Inc.
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