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Predictive Modeling of Mental Health Disorders: AI Applications in Biomarkers and Behavioral Data

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Integrating multimodal datasets significantly improves AI model performance in classifying mental health states.
The use of physiological signals and behavioral indicators enhances diagnostic accuracy for disorders like depression.
This study utilized supervised learning models to analyze complex, multifactorial symptoms of mental health disorders.
AI frameworks can support clinicians in developing individualized care plans and early intervention strategies.

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Exploration of AI applications enhances diagnostic accuracy through biomarkers and behavioral data.

Momand et al. (2025) studied this question.

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