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Research shows deep learning models improve treatment response monitoring in adolescents with major depressive disorder, suggesting advancements in personalized care.
These findings suggest that machine learning models and individual acoustic features may not sufficiently capture treatment-related changes in MDD patients. This study underscores the value of deep learning models using the DVDSA method, addressing the limitations of pre- and post-treatment classification and highlighting their potential to advance personalized treatment strategies for adolescent MDD.
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Kim et al. (2026) studied this question.
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