Multimodal Machine Learning in Mental Health: A Survey of Data, Algorithms, and Challenges
<|reference_start|>Multimodal Machine Learning in Mental Health: A Survey of Data, Algorithms, and Challenges: 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.<|reference_end|>
@article{sahili2024multimodal,
    title={Multimodal Machine Learning in Mental Health: A Survey of Data,
  Algorithms, and Challenges},
    author={Zahraa Al Sahili, Ioannis Patras, Matthew Purver},
    journal={arXiv preprint arXiv:2407.16804},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.16804},
    primaryClass={cs.LG cs.AI cs.CY cs.ET}
}