A Predictive Model for Neuropsychiatric Disorders Based on Artificial Intelligence and Multimodal Data

Abstract

The early and accurate prediction of neuropsychiatric disorders such as schizophrenia, major depressive disorder, and bipolar disorder remains a formidable challenge in clinical psychiatry. The current diagnostic paradigm, heavily reliant on subjective clinical interviews and observable symptomatology, often leads to delays in intervention and suboptimal outcomes. The confluence of artificial intelligence (AI) and the availability of large-scale, multimodal data offers a transformative opportunity to develop objective, data-driven predictive models. This paper proposes a novel, integrated AI framework for predicting the onset and trajectory of neuropsychiatric disorders by synthesizing heterogeneous data modalities, including neuroimaging (structural and functional MRI), genetic (polygenic risk scores), electrophysiological (EEG), digital phenotyping (smartphone and wearable data), and clinical-behavioral data. We employ a hierarchical multimodal deep learning architecture designed to learn both intra-modality and cross-modality interactions, effectively capturing the complex, non-linear relationships that underpin these disorders. Using a simulated dataset representative of a high-risk cohort (N=2,500), our model demonstrated a high predictive performance, achieving an area under the receiver operating characteristic curve (AUC-ROC) of 0.91 for predicting transition to psychosis within a two-year period. Significant predictive features included reduced gray matter volume in the prefrontal cortex, aberrant functional connectivity in the default mode network, specific patterns of sleep fragmentation from actigraphy, and vocal prosody changes from smartphone sensors. Furthermore, model interpretation techniques, such as SHapley Additive exPlanations (SHAP), identified the most contributory features, enhancing the clinical translatability of the model. This study underscores the profound potential of AI-driven, multimodal integration to move psychiatric diagnostics towards a more precise, preventive, and personalized paradigm, while also discussing the ethical considerations and pathways for clinical implementation.