A critical review of methods for aligning and integrating heterogeneous multimodal data (questionnaires, language, EMAs, EEG, voice) in AI models targeting adolescent depression and anxiety.
Research Question: What are the best practices, challenges, and model architectures for aligning and integrating multimodal data in AI models for adolescent mental health prediction and intervention?
Hypothesis: Effective alignment and integration of multimodal data improve the accuracy and interpretability of AI models for adolescent mental health outcomes.
Experiment Plan: Conduct a systematic literature search focusing on recent peer-reviewed articles and high-quality preprints; extract and compare data preprocessing, feature extraction, and alignment strategies; analyze model architectures including transformers and attention mechanisms; synthesize challenges and ethical considerations; identify gaps and future directions; draft a comprehensive review article.
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@misc{雷心宇-comprehensive-review-of-2025,
author = {雷心宇},
title = {Comprehensive Review of Multimodal Data Alignment Techniques for Adolescent Mental Health AI Modeling},
year = {2025},
url = {https://hypogenic.ai/ideahub/idea/kz9jAiLrftevy6iyYEJ5}
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