Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김기범 | - |
dc.date.accessioned | 2024-05-07T01:18:08Z | - |
dc.date.available | 2024-05-07T01:18:08Z | - |
dc.date.issued | 2024-05-03 | - |
dc.identifier.citation | CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE, Page. 1-27 | en_US |
dc.identifier.issn | 1738-1088 | en_US |
dc.identifier.issn | 2093-4327 | en_US |
dc.identifier.uri | https://www.cpn.or.kr/journal/view.html?doi=10.9758/cpn.24.1165 | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/190162 | - |
dc.description.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 electroencephalography (EEG) data. There have been several attempts to differentiate between the diagnoses of bipolar disorder and major depressive disorder , 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 ML 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. | en_US |
dc.description.sponsorship | This research was funded by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI22C0619). This work has also supported by Soonchunhyang University. | en_US |
dc.language | en_US | en_US |
dc.publisher | KOREAN COLL NEUROPSYCHOPHARMACOLOGY | en_US |
dc.subject | Electroencephalography | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Bipolar disorder | en_US |
dc.subject | Major depressive disorder | en_US |
dc.subject | Diagnosis | en_US |
dc.subject | Treatment response | en_US |
dc.title | How to solve clinical challenges in mood disorders | en_US |
dc.title.alternative | Machine Learning Approaches Using Electrophysiological Markers | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.9758/cpn.24.1165 | en_US |
dc.relation.page | 1-27 | - |
dc.relation.journal | CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE | - |
dc.contributor.googleauthor | Song, Young Wook | - |
dc.contributor.googleauthor | Lee, Ho Sung | - |
dc.contributor.googleauthor | Kim, Sungkean | - |
dc.contributor.googleauthor | Kim, Kibum | - |
dc.contributor.googleauthor | Kim, Bin-Na | - |
dc.contributor.googleauthor | Kim, Ji Sun | - |
dc.relation.code | 2024002390 | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF COMPUTING[E] | - |
dc.sector.department | SCHOOL OF MEDIA, CULTURE, AND DESIGN TECHNOLOGY | - |
dc.identifier.pid | kibum | - |
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