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

Aug 5, 2026

Multimodal Machine Learning in Mental Health surveys how combining text, audio, video, and physiological signals enables richer detection, diagnosis, and treatment-oriented insights for mental health disorders. It reviews the data types and representative datasets, the mental health conditions they target, and the state-of-the-art models used, including RNN/CNN, transformer, and graph neural network approaches. The survey also analyzes key barriers such as data availability, privacy risks, bias, benchmarking difficulty, model complexity, and evaluation needs.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Introduction

Data Types and Datasets

Data Types

Challenges and Opportunities

Conclusions

Multimodal Machine Learning in Mental Health: A Survey of Data, Algorithms, and Challenges Zahraa Al Sahili1 * Queen Mary University of London, [z.alsahili@qmul.ac.uk](z.alsahili@qmul.ac.uk)[ ](z.alsahili@qmul.ac.uk)Ioannis Patras Queen Mary University of London, [i.patras@qmul.ac.uk](i.patras@qmul.ac.uk)[ ](i.patras@qmul.ac.uk)Matthew Purver Queen Mary University of London, Jožef Stefan Institute, [m.purver@qmul.ac.uk](m.purver@qmul.ac.uk) 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. CCS CONCEPTS • Artificial intelligence • Machine learning •Applied computing •Life and medical sciences Additional Keywords: multimodal machine learning, mental health, healthcare 1 INTRODUCTION Mental health disorders represent a significant global health concern, with approximately 1 in 8 people worldwide affected by conditions such as anxiety and depression, accounting for an estimated 970 million individuals in 2019 alone [1] . Despite the prevalence of these disorders, there is a substantial treatment gap, particularly in low-and middle-income countries where up to 85% of individuals with mental health conditions receive no treatment [2] . This gap underscores the urgent need for innovative solutions to enhance mental healthcare access and efficacy. In recent years, digital health interventions have emerged as a promising avenue to address this need. The global digital mental health market, valued at $5.1 billion in 2020, is projected to expand at a compound annual growth rate of 21.0% from 2021 to 2028 [3] . Among these interventions, multimodal machine learning—an approach that integrates diverse data types such as text, audio, and video—has shown considerable promise. Research indicates that combining indicates t multiple data types (e.g., audio, video, text) leads to better accuracy and robustness in detecting and assessing mental health conditions compared to using a single data type[4] . For example, integrating facial expressions, speech, and physiological signals can enhance the detection of emotions 1* corresponding author. and mental states, offering more comprehensive insights for interventions speech and facial recognition data can significantly improve the accuracy of detecting mental health conditions, such as depression, compared to single data modality approaches [4] . This survey provides a comprehensive overview of multimodal machine learning in mental health research. We review the various types of multimodal data commonly used, including text, audio, video, and physiological data. We present an overview of available datasets and the specific mental health conditions they address, such as depression, stress, bipolar disorder, and PTSD. Additionally, we discuss state-of-the-art machine learning techniques employed to analyze these data, encompassing RNN/CNN-based, transformer-based, and graph neural network-based algorithms. Furthermore, we explore the

FAQs

What problem does multimodal machine learning address in mental health research?

It integrates multiple modalities (e.g., text, audio, video, physiological signals) to capture behavior patterns and mental health symptoms more comprehensively than single-modality approaches.

Which kinds of data are commonly used in multimodal mental health systems?

Common modalities include text (clinical notes, social media), audio (speech and nonverbal cues), video (facial expressions and body language), and physiological signals (heart rate, skin conductance, EEG).

What major challenges does the survey identify for practical multimodal ML in mental health?

Key challenges include data availability, privacy, bias, benchmarking, system complexity, and evaluation, all of which must be addressed before effective applications can be developed.

Aug 5, 2026

Upload by River Wang