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Applications of deep learning in the identification and classification of mental health status

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Randomized trial implements a deep learning framework for mental health assessment, suggesting improved accuracy over traditional methods.

The research aims to enhance the identification and classification of mental health status through advanced deep learning techniques.
Developed a Chinese-RoBERTa-based multi-head attention fusion sentiment analysis framework (CR-MHA FSA).
Combined hierarchical emotional knowledge with multimodal features and utilized BiLSTM for feature extraction.
Applied focal loss to improve learning from imbalanced emotional categories.
BiLSTM achieved an average F1 score of 0.817 on the EMO-DB task, outperforming VGG (0.745) and LSTM (0.802).
F1 scores for strong positive stimuli during question-answering stages reached 0.5938 and 0.5518.
Pearson similarity between behavioral entropy and depression trends exceeded 72.2%, indicating a strong correlation.

Wang et al. (2026) studied this question.

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Randomized trial implements a deep learning framework for mental health assessment, suggesting improved accuracy over traditional methods.