2OAMN: A Deep Learning Framework for Personalized Psychological Interventions Using Multimodal Student Data - Fu, Jiao; Zhao, Yongjuan (Informatica, 2026)
Background: Student mental health has become a global concern due to increasing academic stress, anxiety, and depression, which negatively impact learning outcomes, emotional well-being, and social interactions. Traditional assessment methods, such as static questionnaires and periodic counseling, fail to capture the dynamic nature of psychological health. Objective: This research aims to develop an intelligent deep learning (DL) framework using neural networks to predict early mental health risks and generate personalized psychological intervention strategies based on students’ evolving emotional states. Methods: The Student Mental Health and Intervention Dataset, comprising 1,000 student records collected through self-reported surveys, physiological indicators were utilized. Data preprocessing involved normalization, sentiment classification using BERT-based text embeddings, and feature extraction with Convolutional Neural Network (CNN) layers. The integrated features were input into the Octopus Optimization with Attention-Based Memory Network (2OAMN). The Bi-LSTM captures both past and future dependencies in mental health data, enhancing prediction accuracy. The attention mechanism prioritizes the most relevant emotional and behavioral signals, ensuring timely interventions. OOA optimizes model parameters by balancing exploration and exploitation, improving adaptability and predictive accuracy. The framework was implemented in Python using DL libraries to predict mental health risks and generate adaptive intervention recommendations. Results: Experimental evaluation shows that 2OAMN achieves 97.86% accuracy, 98.81% precision, 97.64% recall, and 98.22% F1-score. 2OAMN regression performance includes an RMSE of 5.92, MAE of 4.11, and R² of 0.74. Conclusion: The 2OAMN reliably predicts student mental health risks and generates effective personalized interventions, offering a practical and data-driven solution for improving student psychological well-being.