228. Assessing the Predictive Utility of Quantitative Electroencephalography Coherence in Adolescent Major Depressive Disorder: A Machine Learning Approach.

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Title: 228. Assessing the Predictive Utility of Quantitative Electroencephalography Coherence in Adolescent Major Depressive Disorder: A Machine Learning Approach.
Authors: Gersten, Maia1 (AUTHOR), Wade, Benjamin1 (AUTHOR), Briggs, Farren2 (AUTHOR), Loparo, Kenneth3 (AUTHOR), Kaffashi, Farhad3 (AUTHOR), Jamil, Asif1 (AUTHOR), Morales, Oscar1 (AUTHOR), Cassano, Paolo1 (AUTHOR), McVoy, Molly4 (AUTHOR)
Source: Biological Psychiatry. 2025 Supplement, Vol. 97 Issue 9, pS189-S189. 1p.
Database: Academic Search Ultimate
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An: 184125060
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: 228. Assessing the Predictive Utility of Quantitative Electroencephalography Coherence in Adolescent Major Depressive Disorder: A Machine Learning Approach.
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  Data: <searchLink fieldCode="JN" term="%22Biological+Psychiatry%22">Biological Psychiatry</searchLink>. 2025 Supplement, Vol. 97 Issue 9, pS189-S189. 1p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=184125060
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      – Type: doi
        Value: 10.1016/j.biopsych.2025.02.465
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      – Code: eng
        Text: English
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        PageCount: 1
        StartPage: S189
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      – TitleFull: 228. Assessing the Predictive Utility of Quantitative Electroencephalography Coherence in Adolescent Major Depressive Disorder: A Machine Learning Approach.
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              Text: 2025 Supplement
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              Y: 2025
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              Value: 97
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              Value: 9
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            – TitleFull: Biological Psychiatry
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