How to solve clinical challenges in mood disorders; Machine learning approaches using electrophysiological markers
Young Wook Song 1, Ho Sung Lee 2, Sungkean Kim 1, 3, Kibum Kim 3, Bin-Na Kim 4, Ji Sun Kim 5*
1Department of Applied Artificial Intelligence, Hanyang University, Ansan, Republic of Korea, 2Department of Pulmonology and Allergy, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea, 3Department of Human-Computer Interaction, Hanyang University, Ansan, Republic of Korea, 4Department of Psychology, Gachon University, Seongnam, South Korea, 5Department of Psychiatry, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea
Received: January 4, 2024; Revised: March 6, 2024; Accepted: April 1, 2024; Published online: April 1, 2024.
© The Korean College of Neuropsychopharmacology. All rights reserved.

Abstract
Differentiating between the diagnoses of mood disorders and other psychiatric disorders, and predicting treatment response in depression has long been a concern for clinicians. Machine learning (ML) is one part of artificial intelligence that focuses on instructing computers to mimic the cognitive abilities of the human brain through training. This study will review the research on the use of ML techniques to differentiate diagnoses and predict treatment responses in mood disorders based on EEG data. There have been several attempts to differentiate between the diagnoses of BD and MDD, mood disorders, and other psychiatric disorders using ML techniques found on EEG markers. Previous studies have shown that accuracy varies depending on which EEG markers are used, the sample size, and the ML technique. Also, precise and improved machine learning approaches can be developed by adapting the various feature selection and validation methods that reflect each disease's characteristics. Although ML faces some limitations and challenges in solving for consistent and improved accuracy in the diagnosis and treatment of mood disorders, it has a great potential to understand mood disorders better and provide valuable tools to personalize both identification and treatment.
Keywords: Electroencephalography, Machine learning, Bipolar disorder, Major depressive disorder, Diagnosis, Treatment response


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