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Comment
. 2016 Jun 1;73(6):545-6.
doi: 10.1001/jamapsychiatry.2016.0348.

Determining Electroconvulsive Therapy Response With Machine Learning

Christopher C Abbott  1 , Dyani Loo  1 , Jing Sui  2
Affiliations
Comment

Determining Electroconvulsive Therapy Response With Machine Learning

Christopher C Abbott et al. JAMA Psychiatry. .
No abstract available

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Conflict of interest statement

Conflict of Interest Disclosures: None reported.

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References

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    1. Haq AU, Sitzmann AF, Goldman ML, Maixner DF, Mickey BJ. Response of depression to electroconvulsive therapy: a meta-analysis of clinical predictors. The Journal of clinical psychiatry. 2015;76(10):1374–1384. - PubMed
    1. American Psychiatric Association. Consensus report of the APA Work Group on Neuroimaging Markers of Psychiatric Disorders2012, Arlington, VA, USA.
    1. van Waarde JA, Scholte HS, van Oudheusden LJ, Verwey B, Denys D, van Wingen GA. A functional MRI marker may predict the outcome of electroconvulsive therapy in severe and treatment-resistant depression. Molecular psychiatry. 2014. - PubMed
    1. Calhoun VD, Sui J. Multimodal Fusion of Brain Imaging Data: A Key to Finding the Missing Link(s) in Complex Mental Illness. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. 2016;In press. - PMC - PubMed

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