The dynamic assessment of mental health has emerged as a hotspot for study and application due to the rise in social pressure. However, onventional methods rely mostly on static scales or single-modal data, failing to fully capture multifaceted emotional and behavioral features. This study suggests a deep learning model based on multi-modal data fusion to address this problem. By combining information from multiple sources, including text and visuals, the model effectively identifies and dynamically monitors mental states. In the study, a multi-modal dataset containing text and image information is constructed. Mental health levels and emotional tendency labels are generated by combining self-assessment questionnaires and expert annotations. The model uses Bi-LSTM to process the text modality, convolutional neural network (CNN) to process the image modality, and realizes deep feature interaction and integration through an improved multi-modal fusion strategy. Experimental results show that in the emotion recognition task, the precision, recall, and F-score of the proposed model reach 91.3%, 88.9%, and 90.1% respectively. In the classification of "possible depression" and "existing depression", the accuracy rates are 75.89% and 85.32%. All these performance indicators are superior to those of single-modal models and traditional average fusion methods, indicating that multi-modal fusion can significantly improve discriminative ability, robustness, and the accuracy of dynamic assessment. This study's primary contributions are the creation of a jointly annotated dataset, the proposal of a multi-modal deep learning framework appropriate for dynamic mental health assessment, and the confirmation of the fusion strategy's contribution to enhanced assessment performance. The study's findings broaden the range of applications for multi-modal deep learning in the field of mental health and offer a feasible technological solution for public health management, psychological services, and smart healthcare. They also offer theoretical and practical support for early screening, risk early warning, and personalized intervention of mental health.
Mao et al. (Mon,) studied this question.