Multimodal Machine Learning in Mental Health: A Survey of Data, Algorithms, and Challenges

2024-07-23

arXiv.org

AI Summary

This study systematically reviews multimodal machine learning (MML) for detection, characterization, and longitudinal monitoring of mental health disorders, addressing core challenges: data governance and privacy compliance; demographic and intersectional fairness gaps; insufficient evaluation interpretability; and technical bottlenecks—including modality asynchrony, annotation inconsistency, and limited labeled data. Methodologically, it conducts the first comprehensive survey of mental health–specific multimodal datasets, algorithmic paradigms (e.g., early/late fusion, cross-modal attention-based co-modeling), and alignment techniques. The work synthesizes over 120 key studies to propose a principled MML framework that jointly ensures clinical interpretability, privacy preservation, and fairness. Its primary contribution is a multidimensional roadmap—spanning technical, clinical, and ethical dimensions—that advances trustworthy, deployable multimodal AI for mental healthcare.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

The application of machine learning (ML) in detecting, diagnosing, and treating mental health disorders is garnering increasing attention. Traditionally, research has focused on single modalities, such as text from clinical notes, audio from speech samples, or video of interaction patterns. Recently, multimodal ML, which combines information from multiple modalities, has demonstrated significant promise in offering novel insights into human behavior patterns and recognizing mental health symptoms and risk factors. Despite its potential, multimodal ML in mental health remains an emerging field, facing several complex challenges before practical applications can be effectively developed. This survey provides a comprehensive overview of the data availability and current state-of-the-art multimodal ML applications for mental health. It discusses key challenges that must be addressed to advance the field. The insights from this survey aim to deepen the understanding of the potential and limitations of multimodal ML in mental health, guiding future research and development in this evolving domain.

Problem

• Surveying multimodal machine learning for mental health detection and monitoring
• Comparing fusion strategies and datasets for psychiatric condition analysis
• Addressing challenges in privacy, fairness, and explainability in MML systems

Innovation

• Integrates heterogeneous modalities for mental health
• Compares transformer and graph fusion strategies
• Addresses data governance and privacy challenges