A Foundation Model for Patient Behavior Monitoring and Suicide Detection

Foundation models (FMs) have achieved remarkable success across various domains, yet their adoption in healthcare remains limited. While significant advances have been made in medical imaging, genetic biomarkers, and time series from electronic health records, the potential of FMs for patient behavior monitoring through wearable devices remains underexplored. These datasets are inherently heterogeneous, multisource, and often exhibit high rates of missing data, posing unique challenges. This paper introduces a novel FM based on a modified vector quantized variational autoencoder (VQ-VAE), specifically designed to process real-world data from wearable devices. We demonstrate that our pretrained FM, trained on a broad cohort of psychiatric patients, performs downstream tasks via its latent representation without fine-tuning on a held-out cohort of suicidal patients. To illustrate this, we develop a probabilistic change-point detection algorithm for suicide detection and demonstrate the FM's effectiveness in predicting emotional states. Our results show that the discrete latent structure of the VQ-VAE outperforms a state-of-the-art Informer architecture in unsupervised suicide detection, while matching its performance in supervised emotion prediction when the latent dimensionality is increased, though at the cost of reduced unsupervised accuracy. This trade-off highlights the need for future FMs to integrate hybrid discrete-continuous structures for balanced performance across tasks.