How to Solve Clinical Challenges in Mood Disorders; Machine Learning Approaches Using Electrophysiological Markers-Reference-Cited by-同舟云学术

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How to Solve Clinical Challenges in Mood Disorders; Machine Learning Approaches Using Electrophysiological Markers

Published:2024-05-03 Issue:3 Volume:22 Page:416-430
ISSN:1738-1088
Container-title:Clinical Psychopharmacology and Neuroscience
language:en
Short-container-title:Clin Psychopharmacol Neurosci

Author:

Song Young Wook¹^ORCID,Lee Ho Sung²^ORCID,Kim Sungkean¹³^ORCID,Kim Kibum³^ORCID,Kim Bin-Na⁴^ORCID,Kim Ji Sun⁵^ORCID

Affiliation:

1. Department of Applied Artificial Intelligence, Hanyang University, Ansan, Korea

2. Department of Pulmonology and Allergy, Soonchunhyang University Cheonan Hospital, Cheonan, Korea

3. Department of Human-Computer Interaction, Hanyang University, Ansan, Korea

4. Department of Psychology, Gachon University, Seongnam, Korea

5. Department of Psychiatry, Soonchunhyang University Cheonan Hospital, Cheonan, Korea

Funder

Korea Health Industry Development Institute

Ministry of Health and Welfare

Soonchunhyang University

Publisher

Korean College of Neuropsychopharmacology

Link

https://pdf.medrang.co.kr/CPN2/2024/022/cpn022-03-03.pdf

Reference96 articles.

1. Prevalence and Correlates of Psychiatric Disorders in a National Survey of Iranian Children and Adolescents

2. Prevalence and correlates of major depressive disorder: a systematic review

3. One-year psychosocial functioning in patients in the early vs. late stage of bipolar disorder

4. An Overview of Bipolar Disorder Diagnosis Using Machine Learning Approaches: Clinical Opportunities and Challenges

5. Distinguishing bipolar and major depressive disorders by brain structural morphometry: a pilot study

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