Paper Title
MACHINE LEARNING IDENTIFICATION OF LATENT MENTAL HEALTH SPECTRA USING MULTIDIMENSIONAL PSYCHOLOGICAL DATA

Abstract
The intricate and multifarious nature of psychiatric illnesses makes describing mental health conditions a formidable task. With respect to this, the traditional datasets that capture singular aspects of mental health, and largely focus on a small number of specific variables and a single modality, have limited utility in describing the full mental health condition of the individual. Comparatively, the Research Domain Criteria (RDoC) approach, which attempts to capture a full mental health condition, integrates multiple levels of behavioral and neural mechanisms, as well as genetics, to provide a dimensional and mechanistic understanding of mental health that extend beyond diagnosis as defined in the DSM-5. For the purposes of this study, we focus on utilizing a multimodal, semi-supervised and interpretable latent variable approach for the detection of latent variables that represent the range of mental health conditions, which we refer to as spectra, from a collection of heterogeneous data sources. The various modalities, such as neuroimaging, movement, audio, video, questionnaires, etc., will be fused to create a thorough and detailed patient representation. The model architecture consists of modality specific encoders for representation learning, along with a variationalautoencoder (VAE) for the probabilistic fusion of the representations into a single, shared latent space, as well as domain informed projections to augment interpretability and a clinical prediction layer for specific tasks. The goal of the framework is to capture the multidimensional nature of mental health in accord with the RDoC principles by transforming the data from a high-dimensional space into an organized and interpretable latent space. The framework will facilitate refined patient categorization, enhance the precision of clinical outcome predictions, and facilitate the development of personalized, data-driven strategies for mental health interventions. Keywords - Machine Learning, Mental Health, Multidimensional Psychological Data, Research Framework Criteria (RDoC).