Machine learning in major depression: From classification to treatment outcome prediction-Reference-Cited by-同舟云学术

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Machine learning in major depression: From classification to treatment outcome prediction

Published:2018-08-23 Issue:11 Volume:24 Page:1037-1052
ISSN:1755-5930
Container-title:CNS Neuroscience & Therapeutics
language:en
Short-container-title:CNS Neurosci Ther

Author:

Gao Shuang¹²^ORCID,Calhoun Vince D.³⁴^ORCID,Sui Jing¹²⁵^ORCID

Affiliation:

1. Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation; Chinese Academy of Sciences; Beijing China

2. University of Chinese Academy of Sciences; Beijing China

3. The Mind Research Network; Albuquerque New Mexico

4. Department of Electrical and Computer Engineering; The University of New Mexico; Albuquerque New Mexico

5. CAS Centre for Excellence in Brain Science and Intelligence Technology, Institute of Automation; Chinese Academy of Sciences; Beijing China

Funder

National High-Tech Development Plan (863)

National Institutes of Health

Chinese Academy of Sciences

Publisher

Wiley

Subject

Pharmacology (medical),Physiology (medical),Psychiatry and Mental health,Pharmacology

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1111/cns.13048/fullpdf

Reference144 articles.

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2. Managing partial response or nonresponse: switching, augmentation, and combination strategies for major depressive disorder;Papakostas;J Clin Psychiatry,2009

3. Gao S Osuch EA Wammes M Discriminating bipolar disorder from major depression based on kernel SVM using functional independent components 2017

4. Small-world anatomical networks in the human brain revealed by cortical thickness from MRI;He;Cereb Cortex,2007

5. Impaired small-world efficiency in structural cortical networks in multiple sclerosis associated with white matter lesion load;He;Brain,2009

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