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Song, Y. W., Lee, H. S., Kim, S., Kim, K., Kim, B. N., & Kim, J. S. (2024). How to Solve Clinical Challenges in Mood Disorders; Machine Learning Approaches Using Electrophysiological Markers. Clinical Psychopharmacology and Neuroscience: The Official Scientific Journal of the Korean College of Neuropsychopharmacology, 22, 416-430.
https://doi.org/10.9758/cpn.24.1165

has been cited by the following article:

• TITLE: Improving the Diagnosis of Mood Disorders: A Mini-Review of Recent Approaches

  AUTHORS: Cynthia Whissell

  KEYWORDS: Mood Disorders, Diagnostic Issues, New Approaches

  JOURNAL NAME: Open Journal of Depression, Vol.14 No.4, November 28, 2025

  ABSTRACT: This mini-review summarizes information from eight recent (21st century) articles focused on the improvement of diagnoses for mood disorders. Authors of all articles recognized that a problem in diagnosis exists (Table 1) and made suggestions for improvements in existing systems (Table 2). Most of the research employed predictive analytical schemes and many of the proposed diagnostic symptoms were biological (Table 3). A model for success in diagnosis is applied to mood disorders. It is noted that an increase in diagnostic accuracy depends on both better identification of symptoms and better delineation of disorders.